Kyivstar.Tech

ROLE

As a Scrum Master, you will be responsible for facilitating ceremonies and team delivery, removing (and guiding the team to remove) blockers and impediments, assessing and coaching the team to higher levels of Scrum maturity and building a trusting and safe environment. You must demonstrate a strong understanding of SDLC as well as an in-depth knowledge of Scrum principles, practices, and theory.

SKILLS

- At least 2 years of experience as a Scrum Master in app\web development team

- Ability to deliver within each sprint in accordance with the product roadmap

- Guide the team on how to get the most out of self-organisation

- Provide support to the team using a servant leadership style whenever possible

- Agile practices, patterns, and techniques such as automated testing, user stories, TDD, continuous integration, planning etc

- Advanced understanding of task decomposing principles, backlog tracking, burndown metrics, velocity, focus factor, capacity analysis etc.

- Theory and practical knowledge of LeSS framework

- Strong sense of ownership for projects and team performance

- Good skills to coach team how to follow agile scrum, which really works

- Understand the fundamentals of iterative and incremental development

- Excellent verbal, presentation, and written communication skills

- Jira advanced user/admin

- English — Upper Intermediate

TEAM

- You will be joining a team delivering innovative solutions to Products that need your inspired approach and enthusiasm

- You will be joining a most innovative department in biggest Telecommunication company in Ukraine

- You will be working with high motivated professionals on different Products

TOOLS

Jira, Confluence, GitHub, Slack, Outlook, Miro, etc.

WE OFFER:

- Office or remote — it’s up to you: you can work from anywhere, and we will arrange your workplace Remote onboarding

- Performance bonuses for everyone (annual or quarterly — depends on the role)

- We train employees: the opportunity to learn through the company’s own library, internal resources and programs from partners

- Health and life insurance

- Wellbeing program and corporate psychologist

- Reimbursement of expenses for Kyivstar mobile communication

Kyivstar.Tech is looking for VAS planning and development engineer

What you will do:

• Prepare and follow up system strategy: 

- HW and SW lifecycle

- Dimensioning

- Load prediction

- Budgeting and budget execution

• Partner and contract management: 

- Contract scope and terms definition and control (supply, service, NDA)

- Contract technical part preparation (solution description, scope of work, scope of supply)

• Initiate and participate in tender: 

- Technical requirements definition (architecture, features, services, etc.)

- Offers analysis, clarification with participants

- Technical evaluation

• Lead system deployment, system extension, new services (HW/SW/services): 

- Initiate purchase orders

- Coordinate HW supply with partner and logistic function

- Control SW supply, including needed documents preparation 

- Coordinate works with partner, O&M team, other supporting functions: prepare plan, schedule, supervise execution, control 

• Collect business needs and prepare technical requirements for new services/solutions 

• Provide consultancy to business about systems functionality

• Support complex works execution at maintenance window 

Qualifications and experience needed:

• GSM/UMTS/LTE architecture knowledge 

• PS domain elements knowledge: 

- PCRF/PCEF/TDF, DRA, OCS, DPI, GGSN/PGW

- Corresponding 3GPP interfaces

- Signaling protocols: GTP, Diameter 

• IP network knowledge

• English level: Pre-intermediate or higher

• Preferred: 5G architecture knowledge, SQL knowledge, Linux based OSs 

 Target system: 

• PCRF and TDF 

Opportunity: 

• 5G NSA and 5G SA deployment. 

• Participate in large-scale modernization projects. 

• Get wide experience of how business works. 

• Get deep PCRF knowledge. 

• Bring optimization to the existing PCRF system. 

What we offer:

• Office or remote — it's up to you: you can work from anywhere, and we will arrange your workplace

• Remote onboarding

• Performance bonuses for everyone (annual or quarterly — depends on the role)

• We train employees: the opportunity to learn through the company’s own library, internal resources and programs from partners

• Health and life insurance

• Wellbeing program and corporate psychologist

• Reimbursement of expenses for Kyivstar mobile communication

Kyivstar.Tech is seeking an experienced Senior Data Scientist / NLP Lead to spearhead the development of cutting-edge natural language processing solutions for our Ukrainian LLM project. You will lead our NLP team in designing, implementing, and deploying large-scale language models and NLP algorithms that power our products. This role is critical to our mission of advancing AI in the Ukrainian language context, and offers the opportunity to drive technical decisions, mentor a team of data scientists, and shape the future of AI capabilities in Ukraine.

About us

Kyivstar.Tech is a Ukrainian hybrid IT company and a resident of Diia.City.

We are a subsidiary of Kyivstar, one of Ukraine's largest telecom operators.

Our mission is to change lives in Ukraine and around the world by creating technological solutions and products that unleash the potential of businesses and meet users' needs.

Over 500+ KS.Tech specialists work daily in various areas: mobile and web solutions, as well as design, development, support, and technical maintenance of high-performance systems and services.

We believe in innovations that truly bring quality changes and constantly challenge conventional approaches and solutions. Each of us is an adherent of entrepreneurial culture, which allows us never to stop, to evolve, and to create something new.

What you will do 

• Lead end-to-end development of NLP and LLM models - from data exploration and model prototyping to validation and production deployment. This includes designing novel model architectures or fine-tuning state-of-the-art transformer models (e.g. BERT, GPT) to solve project-specific language tasks.

• Analyze large text datasets (Ukrainian and multilingual corpora) to extract insights and build robust training datasets. Guide data collection and annotation efforts to ensure high-quality data for model training.

• Develop and implement NLP algorithms for a range of tasks such as text classification, named entity recognition, semantic search, and conversational AI. Stay up-to-date with the latest research to apply transformer-based models, embeddings, and other modern NLP techniques in our solutions.

• Establish evaluation metrics and validation frameworks for model performance, including accuracy, factuality, and bias. Design A/B tests and statistical experiments to compare model variants and validate improvements.

• Deploy and integrate NLP models into production systems in collaboration with engineers - ensuring models are scalable, efficient, and well-monitored in a real-world setting. Optimize model inference and troubleshoot issues such as model drift or data pipeline bottlenecks.

• Provide technical leadership and mentorship to the NLP/ML team. Review code and research, uphold best practices in ML (version control, reproducibility, documentation), and foster a culture of continuous learning and innovation.

• Collaborate cross-functionally with product managers, software engineers, and MLOps engineers to align NLP solutions with product goals and infrastructure capabilities. Communicate complex data science concepts to stakeholders and incorporate their feedback into the model development process.

Qualifications and experience needed 

Education & Experience:

• 5+ years of experience in data science or machine learning, with a strong focus on NLP.

• Proven track record of developing and deploying NLP or ML models at scale in production environments.

• Advanced degree (Master’s or PhD) in Computer Science, Computational Linguistics, Machine Learning, or a related field is highly preferred.

NLP Expertise:

• Deep understanding of natural language processing techniques and algorithms.

• Hands-on experience with modern NLP approaches, including embedding models, text classification, sequence tagging (NER), and transformers/LLMs.

• Deep understanding of transformer architectures and knowledge of LLM training and fine-tuning techniques, hands-on experience developing solutions on LLM, and knowledge of linguistic nuances in Ukrainian or other languages.

Advanced NLP/ML Techniques:

•Experience with evaluation metrics for language models (perplexity, BLEU, ROUGE, etc.) and with techniques for model optimization (quantization, knowledge distillation) to improve efficiency.

•Background in information retrieval or RAG (Retrieval-Augmented Generation) is a plus for building systems that augment LLMs with external knowledge.

ML & Programming Skills:

•Proficiency in Python and common data science libraries (pandas, NumPy, scikit-learn).

•Strong experience with deep learning frameworks such as PyTorch or TensorFlow for building NLP models.

•Ability to write efficient, clean code and debug complex model issues.

Data & Analytics:

• Solid understanding of data analytics and statistics.

• Experience in experimental design, A/B testing, and statistical hypothesis testing to evaluate model performance.

• Experience in building a representative benchmarking framework given business requirements for LLM.

• Comfortable working with large datasets, writing complex SQL queries, and using data visualization to inform decisions.

Deployment & Tools:

• Experience deploying machine learning models in production (e.g., using REST APIs or batch pipelines) and integrating with real-world applications.

• Familiarity with MLOps concepts and tools (version control for models/data, CI/CD for ML).

• Experience with cloud platforms (AWS, GCP, or Azure) and big data technologies (Spark, Hadoop) for scaling data processing or model training is a plus.

• Hands-on experience with containerization (Docker) and orchestration (Kubernetes) for ML, as well as ML workflow tools (MLflow, Airflow).

Leadership & Communication:

• Demonstrated ability to lead technical projects and mentor junior team members.

• Strong communication skills to convey complex ML results to non-technical stakeholders and to document methodologies.

A plus would be 

LLM training & evaluation experience:

• Experience with tokenizer development, SFT, and RLHF techniques.

• Knowledge of model safety: toxicity, hallucinations, ethical considerations, and LLM guardrails.

Research & Community:

• Publications in NLP/ML conferences or contributions to open-source NLP projects.

• Active participation in the AI community or demonstrated continuous learning (e.g., Kaggle competitions, research collaborations) indicating a passion for staying at the forefront of the field.

Domain & Language Knowledge:

• Familiarity with the Ukrainian language and cultural context for model training and evaluation.

MLOps & Infrastructure:

• Hands-on experience with containerization (Docker) and orchestration (Kubernetes) for ML, as well as ML workflow tools (MLflow, Airflow).

• Experience in working alongside MLOps engineers to streamline the deployment and monitoring of NLP models.

Problem-Solving:

• Creative mindset for tackling open-ended AI challenges.

• Comfort in fast-paced R&D environments with evolving priorities.

What we offer

• Office or remote — it’s up to you. You can work from anywhere, and we will arrange your workplace.

• Remote onboarding.

• Performance bonuses.

• We train employees with the opportunity to learn through the company’s library, internal resources, and programs from partners.

• Health and life insurance.

• Wellbeing program and corporate psychologist.

• Reimbursement of expenses for Kyivstar mobile communication.

We are seeking an experienced Senior/Middle Data Scientist with a passion for large language models (LLMs) and cutting-edge AI research. In this role, you will design and implement a state-of-the-art evaluation and benchmarking framework to measure and guide model quality, and personally train LLMs with a strong focus on Reinforcement Learning from Human Feedback (RLHF). You will work alongside top AI researchers and engineers, ensuring our models are not only powerful but also aligned with user needs, cultural context, and ethical standards. The benchmarks and feedback loops you own serve as the contract for quality—gating releases, catching regressions before users do, and enabling compliant, trustworthy features to ship with confidence.

What you will do

• Analyze benchmarking datasets, define gaps, and design, implement, and maintain a comprehensive benchmarking framework for the Ukrainian language.
• Research and integrate state-of-the-art evaluation metrics for factual accuracy, reasoning, language fluency, safety, and alignment.
• Design and maintain testing frameworks to detect hallucinations, biases, and other failure modes in LLM outputs.
• Develop pipelines for synthetic data generation and adversarial example creation to challenge the model’s robustness.
• Collaborate with human annotators, linguists, and domain experts to define evaluation tasks and collect high-quality feedback.
• Develop tools and processes for continuous evaluation during model pre-training, fine-tuning, and deployment.
• Research and develop best practices and novel techniques in LLM training pipelines.
• Analyze benchmarking results to identify model strengths, weaknesses, and improvement opportunities.
• Work closely with other data scientists to align training and evaluation pipelines.
• Document methodologies and share insights with internal teams.

Qualifications and experience needed

Education & Experience:

• 3+ years of experience in Data Science or Machine Learning, preferably with a focus on NLP.
• Proven experience in machine learning model evaluation and/or NLP benchmarking.
• An advanced degree (Master’s or PhD) in Computer Science, Computational Linguistics, Machine Learning, or a related field is highly preferred.

NLP Expertise:

• Good knowledge of natural language processing techniques and algorithms.
• Hands-on experience with modern NLP approaches, including embedding models, semantic search, text classification, sequence tagging (NER), transformers/LLMs, RAGs.
• Familiarity with LLM training and fine-tuning techniques.

ML & Programming Skills:

• Proficiency in Python and common data science and NLP libraries (pandas, NumPy, scikit-learn, spaCy, NLTK, langdetect, fasttext).
• Strong experience with deep learning frameworks such as PyTorch or TensorFlow for building NLP models.
• Solid understanding of RLHF concepts and related techniques (preference modeling, reward modeling, reinforcement learning).
• Ability to write efficient, clean code and debug complex model issues.

Data & Analytics:

• Solid understanding of data analytics and statistics.
• Experience creating and managing test datasets, including annotation and labeling processes.
• Experience in experimental design, A/B testing, and statistical hypothesis testing to evaluate model performance.
• Comfortable working with large datasets, writing complex SQL queries, and using data visualization to inform decisions.

Deployment & Tools:

• Experience deploying machine learning models in production (e.g., using REST APIs or batch pipelines) and integrating with real-world applications.
• Familiarity with MLOps concepts and tools (version control for models/data, CI/CD for ML).
• Experience with cloud platforms (AWS, GCP, or Azure) and big data technologies (Spark, Hadoop, Ray, Dask) for scaling data processing or model training is a plus.

Communication:

• Experience working in a collaborative, cross-functional environment.
• Strong communication skills to convey complex ML results to non-technical stakeholders and to document methodologies.

A plus would be

Advanced NLP/ML Techniques:

• Prior work on LLM safety, fairness, and bias mitigation.
• Familiarity with evaluation metrics for language models (perplexity, BLEU, ROUGE, etc.) and with techniques for model optimization (quantization, knowledge distillation) to improve efficiency.
• Knowledge of data annotation workflows and human feedback collection methods.

Research & Community:

• Publications in NLP/ML conferences or contributions to open-source NLP projects.
• Active participation in the AI community or demonstrated continuous learning (e.g., Kaggle competitions, research collaborations) indicates a passion for staying at the forefront of the field.

Domain & Language Knowledge:

• Familiarity with the Ukrainian language and context.
• Understanding of cultural and linguistic nuances that could inform model training and evaluation in a Ukrainian context.
• Knowledge of Ukrainian benchmarks, or familiarity with other evaluation datasets and leaderboards for large models, can be an advantage given our project’s focus.

MLOps & Infrastructure:

• Hands-on experience with containerization (Docker) and orchestration (Kubernetes) for ML, as well as ML workflow tools (MLflow, Airflow).
• Experience in working alongside MLOps engineers to streamline the deployment and monitoring of NLP models.

Problem-Solving:

• Innovative mindset with the ability to approach open-ended AI problems creatively.
• Comfort in a fast-paced R&D environment where you can adapt to new challenges, propose solutions, and drive them to implementation.

What we offer:

• Office or remote — it’s up to you. You can work from anywhere, and we will arrange your workplace.
• Remote onboarding.
• Performance bonuses for everyone (annual or quarterly — depends on the role).
• We train employees with the opportunity to learn through the company’s library, internal resources, and programs from partners. 
• Health and life insurance.
• Wellbeing program and corporate psychologist.
• Reimbursement of expenses for Kyivstar mobile communication.

We are seeking an experienced Senior/Middle Data Scientist with a passion for large language models (LLMs) and cutting-edge AI research. In this role, you will focus on designing and prototyping data preparation pipelines, collaborating closely with data engineers to transform your prototypes into scalable production pipelines and actively develop model training pipelines with other talented data scientists. Your work will directly shape the quality and capabilities of our models by ensuring we feed them the highest-quality, most relevant data possible. The datasets you build directly determine model capability, safety, and cost, raising downstream task accuracy, reducing training waste, and accelerating time-to-market for product teams.

What you will do

• Design, prototype, and validate data preparation and transformation steps for LLM training datasets, including cleaning and normalization of text, filtering of toxic content, de-duplication, de-noising, detection and deletion of personal data, etc.
• Formation of specific SFT/RLHF datasets from existing data, including data augmentation/labeling with LLM as teacher.
• Analyze large-scale raw text, code, and multimodal data sources for quality, coverage, and relevance.
• Develop heuristics, filtering rules, and cleaning techniques to maximize training data effectiveness.
• Collaborate with data engineers to hand over prototypes for automation and scaling.
• Research and develop best practices and novel techniques in LLM training pipelines.
• Monitor and evaluate data quality impact on model performance through experiments and benchmarks.
• Research and implement best practices in large-scale dataset creation for AI/ML models.
• Document methodologies and share insights with internal teams.

Qualifications and experience needed

Education & Experience:

• 3+ years of experience in Data Science or Machine Learning, preferably with a focus on NLP.
• Proven experience in data preprocessing, cleaning, and feature engineering for large-scale datasets of unstructured data (text, code, documents, etc.).
• An advanced degree (Master’s or PhD) in Computer Science, Computational Linguistics, Machine Learning, or a related field is highly preferred.

NLP Expertise:

• Good knowledge of natural language processing techniques and algorithms.
• Hands-on experience with modern NLP approaches, including embedding models, semantic search, text classification, sequence tagging (NER), transformers/LLMs, RAGs.
• Familiarity with LLM training and fine-tuning techniques, and data requirements.

ML & Programming Skills:

• Proficiency in Python and common data science and NLP libraries (pandas, NumPy, scikit-learn, spaCy, NLTK, langdetect, fasttext).
• Strong experience with deep learning frameworks such as PyTorch or TensorFlow for building NLP models.
• Ability to write efficient, clean code and debug complex model issues.

Data & Analytics:

• Solid understanding of data analytics and statistics.
• Experience in experimental design, A/B testing, and statistical hypothesis testing to evaluate model performance.
• Comfortable working with large datasets, writing complex SQL queries, and using data visualization to inform decisions.

Deployment & Tools:

• Experience deploying machine learning models in production (e.g., using REST APIs or batch pipelines) and integrating with real-world applications.
• Familiarity with MLOps concepts and tools (version control for models/data, CI/CD for ML).
• Experience with cloud platforms (AWS, GCP, or Azure) and big data technologies (Spark, Hadoop, Ray, Dask) for scaling data processing or model training is a plus.

Communication & Personality:

• Experience working in a collaborative, cross-functional environment.
• Strong communication skills to convey complex ML results to non-technical stakeholders and to document methodologies.
• Ability to rapidly prototype and iterate on ideas

A plus would be

Advanced NLP/ML Techniques:

• Familiarity with evaluation metrics for language models (perplexity, BLEU, ROUGE, etc.) and with techniques for model optimization (quantization, knowledge distillation) to improve efficiency.
• Understanding of FineWeb2 or a similar processing pipeline approach

Research & Community:

• Publications in NLP/ML conferences or contributions to open-source NLP projects.
• Active participation in the AI community or demonstrated continuous learning (e.g., Kaggle competitions, research collaborations)

Domain & Language Knowledge:

• Familiarity with the Ukrainian language and context.
• Understanding of cultural and linguistic nuances that could inform model training and evaluation in a Ukrainian context.
• Knowledge of Ukrainian text sources and data sets, or experience with multilingual data processing, can be an advantage given our project’s focus.

MLOps & Infrastructure:

• Hands-on experience with containerization (Docker) and orchestration (Kubernetes) for ML, as well as ML workflow tools (MLflow, Airflow).
• Experience in working alongside MLOps engineers to streamline the deployment and monitoring of NLP models.

Problem-Solving:

• Innovative mindset with the ability to approach open-ended AI problems creatively.
• Comfort in a fast-paced R&D environment where you can adapt to new challenges, propose solutions, and drive them to implementation.

What we offer

• Office or remote — it’s up to you. You can work from anywhere, and we will arrange your workplace.
• Remote onboarding.
• Performance bonuses.
• We train employees with the opportunity to learn through the company’s library, internal resources, and programs from partners.
• Health and life insurance.
• Wellbeing program and corporate psychologist.
• Reimbursement of expenses for Kyivstar mobile communication.

We are hiring an MLOps Engineer specializing in Large Language Model (LLM) infrastructure to design and maintain the robust platform on which our AI models are developed, deployed, and monitored. As an MLOps Engineer, you will build the backbone of our machine learning operations — from scalable training pipelines to reliable deployment systems — ensuring that our NLP models (including LLMs) can be trained on large datasets and served to end-users efficiently. This role sits at the intersection of software engineering, DevOps, and machine learning, and is crucial for accelerating our R&D in the Ukrainian LLM project. You’ll work closely with data scientists and software engineers to implement best-in-class infrastructure and workflows for the continuous delivery of AI innovations.

What you will do

• Design and implement modern, scalable ML infrastructure (cloud-native or on-premises) to support both experimentation and production deployment of NLP/LLM models. This includes setting up systems for distributed model training (leveraging GPUs or TPUs across multiple nodes) and high-throughput model serving (APIs, microservices).
• Develop end-to-end pipelines for model training, validation, and deployment. Automate the ML workflow from data ingestion and feature processing to model training and evaluation, using technologies like Docker and CI/CD pipelines to ensure reproducibility and reliability.
• Collaborate with Data Scientists and ML Engineers to design MLOps solutions that meet model performance and latency requirements. Architect deployment patterns (batch, real-time, streaming inference) are appropriate for various use-cases (e.g., a real-time chatbot vs. offline analysis).
• Implement and uphold best practices in MLOps, including automated testing of ML code, continuous integration/continuous deployment for model updates, and rigorous version control for code, data, and model artifacts. Ensure every model and dataset is properly versioned and reproducible.
• Set up monitoring and alerting for deployed models and data pipelines. Use tools to track model performance (latency, throughput) and accuracy drift in production. Implement logging and observability frameworks to quickly detect anomalies or degradations in model outputs.
• Manage and optimize our Kubernetes-based deployment environments. Containerize ML services and use orchestration (Kubernetes, Docker Swarm or similar) to scale model serving infrastructure. Handle cluster provisioning, health, and upgrades, possibly using Helm charts for managing LLM services.
• Maintain infrastructure-as-code (e.g., Terraform, Ansible) for provisioning cloud resources and ML infrastructure, enabling reproducible and auditable changes to the environment. Ensure our infrastructure is scalable, cost-effective, and secure.
• Perform code reviews and guide other engineers (both MLOps and ML developers) on building efficient and maintainable pipelines. Troubleshoot issues across the ML lifecycle, from data processing bottlenecks to model deployment failures, and continuously improve system robustness.

Qualifications and experience needed

Experience & Background:

• 4+ years of experience in DevOps, MLOps, or ML Infrastructure roles
• Strong foundation in software engineering and DevOps principles as they apply to machine learning
• A bachelor’s or Master’s in Computer Science, Engineering, or a related field is preferred

Cloud & Infrastructure:

• Extensive experience with cloud platforms (AWS, GCP, or Azure) and designing cloud-native applications for ML
• Comfortable using cloud services for compute (EC2, GCP Compute, Azure VMs), storage (S3, Cloud Storage), container registry, and serverless components where appropriate
• Experience managing infrastructure with Infrastructure-as-Code tools like Terraform or CloudFormation

Containerization & Orchestration:

• Proficiency in container technologies (Docker) and orchestration with Kubernetes
• Ability to deploy, scale, and manage complex applications on Kubernetes clusters; experience with tools like Helm for Kubernetes package management
• Knowledge of container security and networking basics in distributed systems

CI/CD & Automation:

• Strong experience implementing CI/CD pipelines for ML projects
• Familiar with tools like Jenkins, GitLab CI, or GitHub Actions for automating testing and deployment of ML code and models
• Experience with specialized ML CI/CD (e.g., TensorFlow Extended TFX, MLflow for model deployment) and GitOps workflows (Argo CD) is a plus

Programming & Scripting:

• Strong coding skills in Python, with experience in writing pipelines or automation scripts related to ML tasks
• Familiarity with shell scripting and one or more general-purpose languages (Go, Java, or C++) for infrastructure tooling
• Ability to debug and optimize code for performance (both in data pipelines and in model inference code)

ML Pipeline Knowledge:

• Solid understanding of the machine learning lifecycle and tools
• Experience building or maintaining ML pipelines, possibly using frameworks like Kubeflow, Airflow, or custom solutions
• Knowledge of model serving frameworks (TensorFlow Serving, TorchServe, NVIDIA Triton, or custom Flask/FastAPI servers for ML)

Monitoring & Reliability:

• Experience setting up monitoring for applications and models (using Prometheus, Grafana, CloudWatch, or similar) and implementing alerting for anomalies
• Understanding of model performance metrics and how to track them in production (e.g., accuracy on a validation stream, response latency)
• Familiarity with concepts of A/B testing or canary deployments for model updates in production

Security & Compliance:

• Basic understanding of security best practices in ML deployments, including data encryption, access control, and dealing with sensitive data in compliance with regulations
• Experience implementing authentication/authorization for model endpoints and ensuring infrastructure complies with organizational security policies

Team Collaboration:

• Excellent collaboration skills to work with cross-functional teams
• Experience interacting with data scientists to translate model requirements into scalable infrastructure
• Strong documentation habits for outlining system designs, runbooks for operations, and lessons learned

A plus would be

LLM/AI Domain Experience:

• Previous experience deploying or fine-tuning large language models or other large-scale deep learning models in production
• Knowledge of specialized optimizations for LLMs (such as model parallelism, quantization techniques like 8-bit or 4-bit quantization, and use of libraries like DeepSpeed or Hugging Face Accelerate for efficient training) will be highly regarded

Distributed Computing:

• Experience with distributed computing frameworks such as Ray for scaling up model training across multiple nodes
• Familiarity with big data processing (Spark, Hadoop) and streaming data (Kafka, Flink) to support feeding data into ML systems in real time

Data Engineering Tools:

• Some experience with data pipeline and ETL
• Knowledge of tools like Apache Airflow, Kafka, or dbt, and how they integrate into ML pipelines
• Understanding of data warehousing concepts (Snowflake, BigQuery) and how processed data is used for model training

Versioning & Experiment Tracking:

• Experience with ML experiment tracking and model registry tools (e.g., MLflow, Weights & Biases, DVC)
• Ensuring that every model version and experiment is logged and reproducible for auditing and improvement cycles

Vector Databases & Retrieval:

• Familiarity with vector databases (Pinecone, Weaviate, FAISS) and retrieval systems used in conjunction with LLMs for augmented generation is a plus

High-Performance Computing:

• Exposure to HPC environments or on-prem GPU clusters for training large models
• Understanding of how to maximize GPU utilization, manage job scheduling (with tools like Slurm or Kubernetes operators for ML), and profile model performance to remove bottlenecks

Continuous Learning:

• Up-to-date with the latest developments in MLOps and LLMOps (Large Model Ops)
• Active interest in new tools or frameworks in the MLOps ecosystem (e.g., model optimization libraries, new orchestration tools) and a drive to evaluate and introduce them to improve our processes

What we offer

• Office or remote — it’s up to you. You can work from anywhere, and we will arrange your workplace
• Remote onboarding
• Performance bonuses
• We train employees with the opportunity to learn through the company’s library, internal resources, and programs from partners
• Health and life insurance
• Wellbeing program and corporate psychologist
• Reimbursement of expenses for Kyivstar mobile communication

Kyivstar.Tech team is looking for a new colleague for the role of ETL/RAID developer

What you will do

Development of functionality and ensuring the operation of processes:

• Creation of orders and aggregation processes using ETL/ WEDO RAID  
• Development of processes related to data processing, interaction with systems and support of existing processes 
• Testing processes and logic developed in streams
• Work on writing and correcting Batch file, Java-script, Python-script, work with API, CSV, TXT, XML, JSON

• Administration of test environments + provision of recommendations for process changes

   

Qualifications and experience needed

• At least 4 years of experience with SQL programming and development using ETL/ WEDO RAID tools

• Knowledge of Python, Java or similar programming languages ​​will be an advantage

   

What we offer

• Office or remote — it's up to you: you can work from anywhere, and we will arrange your workplace
• Remote onboarding
• Performance bonuses for everyone (annual or quarterly — depends on the role)
• We train employees: with the opportunity to learn through the company’s library, internal resources, and programs from partners
• Health and life insurance
• Wellbeing program and corporate psychologist
• Reimbursement of expenses for Kyivstar mobile communication

We are looking for a Data Engineer to build and optimize the data pipelines that fuel our Ukrainian LLM and Kyivstar’s NLP initiatives. In this role, you will design robust ETL/ELT processes to collect, process, and manage large-scale text and metadata, enabling our data scientists and ML engineers to develop cutting-edge language models. You will work at the intersection of data engineering and machine learning, ensuring that our datasets and infrastructure are reliable, scalable, and tailored to the needs of training and evaluating NLP models in a Ukrainian language context. This is a unique opportunity to shape the data foundation of a pioneering AI project in Ukraine, working alongside NLP experts and leveraging modern big data technologies.

What you will do

• Design, develop, and maintain ETL/ELT pipelines for gathering, transforming, and storing large volumes of text data and related information. Ensure pipelines are efficient and can handle data from diverse sources (e.g., web crawls, public datasets, internal databases) while maintaining data integrity.
• Implement web scraping and data collection services to automate the ingestion of text and linguistic data from the web and other external sources. This includes writing crawlers or using APIs to continuously collect data relevant to our language modeling efforts.
• Implementation of NLP/LLM-specific data processing: cleaning and normalization of text, like filtering of toxic content, de-duplication, de-noising, detection, and deletion of personal data.
• Formation of specific SFT/RLHF datasets from existing data, including data augmentation/labeling with LLM as teacher.
• Set up and manage cloud-based data infrastructure for the project. Configure and maintain data storage solutions (data lakes, warehouses) and processing frameworks (e.g., distributed compute on AWS/GCP/Azure) that can scale with growing data needs.
• Automate data processing workflows and ensure their scalability and reliability. Use workflow orchestration tools like Apache Airflow to schedule and monitor data pipelines, enabling continuous and repeatable model training and evaluation cycles.
• Maintain and optimize analytical databases and data access layers for both ad-hoc analysis and model training needs. Work with relational databases (e.g., PostgreSQL) and other storage systems to ensure fast query performance and well-structured data schemas.
• Collaborate with Data Scientists and NLP Engineers to build data features and datasets for machine learning models. Provide data subsets, aggregations, or preprocessing as needed for tasks such as language model training, embedding generation, and evaluation.
• Implement data quality checks, monitoring, and alerting. Develop scripts or use tools to validate data completeness and correctness (e.g., ensuring no critical data gaps or anomalies in the text corpora), and promptly address any pipeline failures or data issues. Implement data version control.
• Manage data security, access, and compliance. Control permissions to datasets and ensure adherence to data privacy policies and security standards, especially when dealing with user data or proprietary text sources.

Qualifications and experience needed

• Education & Experience: 3+ years of experience as a Data Engineer or in a similar role, building data-intensive pipelines or platforms. A Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field is preferred. Experience supporting machine learning or analytics teams with data pipelines is a strong advantage.
• NLP Domain Experience: Prior experience handling linguistic data or supporting NLP projects (e.g., text normalization, handling different encodings, tokenization strategies). Knowledge of Ukrainian text sources and data sets, or experience with multilingual data processing, can be an advantage given our project’s focus. Understanding of FineWeb2 or a similar processing pipeline approach.
• Data Pipeline Expertise: Hands-on experience designing ETL/ELT processes, including extracting data from various sources, using transformation tools, and loading into storage systems. Proficiency with orchestration frameworks like Apache Airflow for scheduling workflows. Familiarity with building pipelines for unstructured data (text, logs) as well as structured data.
• Programming & Scripting: Strong programming skills in Python for data manipulation and pipeline development. Experience with NLP packages (spaCy, NLTK, langdetect, fasttext, etc.). Experience with SQL for querying and transforming data in relational databases. Knowledge of Bash or other scripting for automation tasks. Writing clean, maintainable code and using version control (Git) for collaborative development.
• Databases & Storage: Experience working with relational databases (e.g., PostgreSQL, MySQL), including schema design and query optimization. Familiarity with NoSQL or document stores (e.g., MongoDB) and big data technologies (HDFS, Hive, Spark) for large-scale data is a plus. Understanding of or experience with vector databases (e.g., Pinecone, FAISS) is beneficial, as our NLP applications may require embedding storage and fast similarity search.
• Cloud Infrastructure: Practical experience with cloud platforms (AWS, GCP, or Azure) for data storage and processing. Ability to set up services such as S3/Cloud Storage, data warehouses (e.g., BigQuery, Redshift), and use cloud-based ETL tools or serverless functions. Understanding of infrastructure-as-code (Terraform, CloudFormation) to manage resources is a plus.
• Data Quality & Monitoring: Knowledge of data quality assurance practices. Experience implementing monitoring for data pipelines (logs, alerts) and using CI/CD tools to automate pipeline deployment and testing. An analytical mindset to troubleshoot data discrepancies and optimize performance bottlenecks.
• Collaboration & Domain Knowledge: Ability to work closely with data scientists and understand the requirements of machine learning projects. Basic understanding of NLP concepts and the data needs for training language models, so you can anticipate and accommodate the specific forms of text data and preprocessing they require. Good communication skills to document data workflows and to coordinate with team members across different functions.

A plus would be

• Advanced Tools & Frameworks: Experience with distributed data processing frameworks (such as Apache Spark or Databricks) for large-scale data transformation, and with message streaming systems (Kafka, Pub/Sub) for real-time data pipelines. Familiarity with data serialization formats (JSON, Parquet) and handling of large text corpora.
• Web Scraping Expertise: Deep experience in web scraping, using tools like Scrapy, Selenium, or Beautiful Soup, and handling anti-scraping challenges (rotating proxies, rate limiting). Ability to parse and clean raw text data from HTML, PDFs, or scanned documents.
• CI/CD & DevOps: Knowledge of setting up CI/CD pipelines for data engineering (using GitHub Actions, Jenkins, or GitLab CI) to test and deploy changes to data workflows. Experience with containerization (Docker) to package data jobs and with Kubernetes for scaling them is a plus.
• Big Data & Analytics: Experience with analytics platforms and BI tools (e.g., Tableau, Looker) used to examine the data prepared by the pipelines. Understanding of how to create and manage data warehouses or data marts for analytical consumption.
• Problem-Solving: Demonstrated ability to work independently in solving complex data engineering problems, optimising existing pipelines, and implementing new ones under time constraints. A proactive attitude to explore new data tools or techniques that could improve our workflows.

What we offer

• Office or remote – it’s up to you. You can work from anywhere, and we will arrange your workplace.
• Remote onboarding.
• Performance bonuses.
• We train employees with the opportunity to learn through the company’s library, internal resources, and programs from partners.   
• Health and life insurance.  
• Wellbeing program and corporate psychologist.  
• Reimbursement of expenses for Kyivstar mobile communication.  

We are looking for a Business Analyst to join our Product Development Team. You will join the in-house development team, whose primary responsibility is analyzing and designing scenarios for B2B chat bot via Node-red. The role has all the advantages of working in a product team — versatile projects, plenty of independence, a chance to influence the direction of development, and the opportunity to grow with the role.

About us

Kyivstar.Tech is a Ukrainian hybrid IT company and a resident of Diia.City.

We are a subsidiary of Kyivstar, one of Ukraine’s largest telecom operators.

Our mission is to change lives in Ukraine and around the world by creating technological solutions and products that unleash the potential of businesses and meet users’ needs.

Over 500+ KS.Tech specialists work daily in various areas: mobile and web solutions, as well as design, development, support, and technical maintenance of high-performance systems and services.

We believe in innovations that truly bring quality changes and constantly challenge conventional approaches and solutions. Each of us is an adherent of entrepreneurial culture, which allows us never to stop, to evolve, and to create something new.

What you will do

• Work in a cross-functional team with analysts, developers & designers to deliver product features
• Identifying options for potential solutions and assessing them for technical and business suitability
• Working closely with the Product Manager, Development team, IT, external stakeholders, and end-users to ensure technical compatibility and user satisfaction
• Analyze requirements, and define use cases and functional requirements for the software
• Design chat bot scenarios for B2B users
• Creates and updates functional & technical descriptions of business processes related to supporting, configuration, and maintenance of the solution
• Perform requirements management, prioritization, traceability, verification, and validation of the requirements
• Backlog management and facilitation if needed
• Oversees test implementation, gathers feedback from users, and proposes functional improvements

• Selection and implementation of the necessary tools, technologies, and techniques.

   

Qualifications and experience needed

• 2+ years of relevant working experience
• Experience producing solution artifacts, including UML, Use Cases, Business Rules, Functional & Non-Functional Specifications, etc
• Good knowledge of Scrum methodology and understanding of the BA role in the Scrum process
• Good understanding of web standards, protocols, messaging protocols, and data formats (REST, SOAP, JSON, XML, HTTP, FTP, TCP, etc.)
• Understanding of low code engineering
• Experience with configuration in Node-red
• Strong understanding of SDLC principles
• Understanding of CI/CD (principles, instruments)
• Good communication & facilitation skills
• Strong analytical and system thinking skills
• Team player with proven ability to work cross-functionally

• Can effectively deal with ambiguity

   

A plus would be

• Experience in low code programming
• Experience with data sources such as PostgreSQL, MySQL, MongoDB, or MS SQL servers
• Understanding and experience applying SOA, DDD, monolith, and design patterns
• Experience in designing RESTful APIs
• Basic expertise in mobile development technologies
• Experience in integration projects

• Excellent knowledge of Atlassian stack (Jira, Confluence)

   

What we offer

• Office or remote — it’s up to you. You can work from anywhere, and we will arrange your workplace
• Remote onboarding
• Performance bonuses
• We train employees with the opportunity to learn through the company’s library, internal resources, and programs from partners
• Health and life insurance
• Wellbeing program and corporate psychologist
• Reimbursement of expenses for Kyivstar mobile communication

Команда Київстар.Тех в пошуку нового колеги або коліжанки на роль Інженера технічної підтримки системи One Identity Management.

Що ти будеш робити:

1. Забезпечувати технічну підтримку системи One Identity Management. Система надає централізоване управління обліковими записами, правами доступу, ролями, політиками безпеки та відповідністю контролів

2. Виконувати адміністративні роботи в системі (усунення помилок, інцидентів, відновлення роботи системи, адміністрування користувачів, виконання заявок користувачів)

3. Керувати основним функціоналом системи One Identity Management:

  a. Конфігурування системи 

  b. Управління доступом

  c. Управлення ролями

  d. Інтеграція з каталогами та системами (Active Directory, Azure AD, SAP, SQL Server, Oracle, Unix/Linux)

  f. Конфігурування механізму затвердження та workflow

4. Створювати оперативні звіти з системи One Identity Management 

5. Розробляти макрокоманди, які необхідні для автоматизації процесів в системі

6. Налаштовувати метрики моніторингу, контроль за виконанням резервування системи

7. Встановлювати, тестувати нові патчі, оновлення системи One Identity Management  

8. Взаємодіяти з розробниками системи, контроль виконання поставлених завдань розробникам

9. Брати участь в проєктах з розвитку системи

Яка кваліфікація та досвід необхідні 

Досвід адміністрування IT систем (від 3 років)

Досвід адміністрування One Identity Management (як перевага)

Глибокі навички роботи з СУБД MS SQL, володіння T-SQL чи PL/SQL (написання скриптів, управління об'єктами БД) 

Глибокі навички роботи з IIS (Internet Information Services)

Досвід роботи з ОС Windows Server та Linux

Англійська мова – upper intermediate (B2)

Що ми пропонуємо 

Офіс або ремоут – вирішувати тобі. Ми даємо можливість працювати будь-де, а робоче місце облаштуємо 

Ремоут онбординг  

Перформанс бонуси для всіх (річні чи квартальні — залежить від ролі)  

Навчаємо працівників: є безліч внутрішніх ресурсів і програм від партнерів, власна бібліотека  

Страхування здоров’я і життя для працівників  

Wellbeing-програма та корпоративний психолог  

Компенсація витрат на мобільний зв'язок Київстар 

Київстар.Тех відкриває нову роль AI/ML Developer у Відділі систем взаємодії з клієнтами.

Обов’язкові навички та досвід:

• 2+ років комерційного досвіду з Python .
• Досвід з фреймворками ML: TensorFlow, PyTorch, ONNX, HuggingFace.
• Практика створення, навчання, адаптації та розгортання ML-моделей (NLP, speech recognition).
• Досвід з LLM і transformer-моделями: fine-tuning, RAG, генерація відповідей/дій.
• Інженерія даних: feature engineering, data preprocessing/postprocessing, валідація даних.
• Розуміння ML/DL-підходів, циклу дослідження та розробки моделей.
• Розгортання моделей у production (FastAPI, Docker, Kubernetes).
• Розробка inference/training/monitoring пайплайнів (у хмарі або on-prem).
• Інтеграція з хмарними AI-платформами: Google Vertex AI, OpenAI, Azure AI, AWS Bedrock, Claude тощо.
• Розробка API-сервісів, що обгортають моделі та забезпечують їхню продакшн-готовність (стабільність, масштабованість)
• Англійська - B1 Intermediate

Буде перевагою:

• Досвід з аналітичними, транзакційними, гібридними та векторними базами даних (BigQuery, ClickHouse, Pinecone, FAISS, Weaviate тощо).
• Знання розробки фронтенд частини веб додатків
• Досвід генерації документів або відповідей за допомогою LLM.
• Побудова AI-агентів: Langchain, CrewAI, AutoGen, open-agents.
• Знання LangGraph, Semantic Kernel, Memory Graph.
• Розуміння архітектури багатоагентних систем.
• Досвід інтеграції LLM у корпоративні продукти.
• Знання DevOps/MLOps-підходів.

Обов’язки

• Проведення досліджень, аналіз даних, побудова гіпотез.
• Побудова та координація AI-агентів для автоматизації логістичних, клієнтських та бек-офіс процесів.
• Тренування, налаштування та розгортання моделей у production.
• Розробка API для обгортання моделей, забезпечення масштабованості та стабільності.
• Співпраця з іншими членами команди.
• Участь у формуванні AI-стратегії компанії, експериментах та прототипуванні.

Ми пропонуємо

• Офіс або ремоут — вирішувати тобі. Ми даємо можливість працювати будь-де, а робоче місце облаштуємо
• Ремоут онбординг
• Перформанс бонуси для всіх (річні чи квартальні — залежить від ролі)
• Навчаємо працівників: є безліч внутрішніх ресурсів і програм від партнерів, власна бібліотека
• Страхування здоров’я і життя для працівників
• Wellbeing-програма та корпоративний психолог
• Компенсація витрат на мобільний зв’язок Київстар

Наша команда продовжує зростати, аби створювати круті рішення для наших внутрішніх клієнтів.

Тому ми знову в пошуку Microsoft Power Platform Developer, який приєднається до нас для налаштування та підтримки автоматичних потоків у середовищі Microsoft Power Platform, а також розвитку та підтримки внутрішнього чат-бота компанії.

Про нас 

Kyivstar.Tech – українська гібридна ІТ-компанія, резидент Дія.City. Ми є дочірньою компанією Київстар, одного з найбільших українських операторів зв'язку. 

Наша місія – змінювати життя в Україні та в світі, створюючи технологічні рішення і продукти, що реалізують потенціал компаній і потреби користувачів. 

Понад 500 спеціалістів KS.Tech щодня працюють у різних сферах: мобільні та веб-рішення, а також проєктування, розробка, підтримка та технічне обслуговування високопродуктивних систем і сервісів. 

Ми віримо в інновації, що дійсно приносять якісні зміни, та постійно кидаємо виклик традиційним підходам і рішенням. Кожен з нас є адептом підприємницької культури, яка дозволяє ніколи не зупинятися, розвиватися і створювати нове. 

Що ти будеш робити

• Працювати з продуктами Microsoft Power Platform - Power Apps, Power Automate
• Підтримувати існуючі додатки на базі PowerApps
• Підтримувати існуючі процесів на базі Power Automate
• Створювати та вдосконалювати автоматизовані процеси на базі Power Automate
• Працювати із внутрішніми клієнтами

Яка кваліфікація та досвід необхідні

• Досвід роботи з Power Automate, Power Apps
• Досвід розробки рішень для служб Office365 (SharePoint, Outlook, OneNote. One Drive тощо)
• Розуміння роботи реляційних баз даних, досвід роботи з SQL та Dataverse

Плюсом стане

• Навчання та/або сертифікація по продуктам Power Platform

Що ми пропонуємо

• Офіс або ремоут – вирішувати тобі. Ми даємо можливість працювати будь-де, а робоче місце облаштуємо 
• Ремоут онбординг  
• Перформанс бонуси для всіх (річні чи квартальні — залежить від ролі)  
• Навчаємо працівників: є безліч внутрішніх ресурсів і програм від партнерів, власна бібліотека  
• Страхування здоров’я і життя для працівників  
• Wellbeing-програма та корпоративний психолог  
• Компенсація витрат на мобільний зв'язок Київстар 

Команда інфраструктурних рішень Київстар.Тех в пошуку нового колеги або коліжанки на роль системного адміністратора.

Що ти будеш робити

• Підтримувати та адмініструвати серверні операційні системи (Windows Server, Linux)
• Встановлювати, налаштувати та оновлювати серверне програмне забезпечення
• Адмініструвати бази даних PostgreSQL та MS SQL Server, а саме: встановлення, налаштування, оптимізація, резервне копіювання та відновлення
• Займатись моніторингом та забезпеченням безпеки серверів і баз даних
• Відповідати за управління користувацькими обліковими записами та правами доступу
• Діагностика та усунення несправностей, пов’язаних із серверним обладнанням та програмним забезпеченням
• Підтримувати процедури резервного копіювання та відновлення даних
• Забезпечувати актуальності документації щодо конфігурацій та процесів
• Планувати та виконувати оновлення серверів та баз даних

• Взаємодіяти з розробниками та користувачами для вирішення проблем, пов'язаних із продуктивністю та доступністю систем

   

Яка кваліфікація та досвід необхідні

• Досвід роботи системним адміністратором від 3 років
• Впевнені знання серверних операційних систем (Windows Server, Linux)
• Розуміння принципів роботи мереж та протоколів (TCP/IP, DNS, DHCP, VPN)
• Досвід роботи з системами моніторингу та управління інцидентами
• Знання скриптових мов (наприклад, PowerShell, Bash) для автоматизації завдань
• Досвід роботи з віртуалізацією (VMware, Hyper-V)
• Розуміння принципів безпеки IT-інфраструктури та баз даних
• Здатність працювати в умовах багатозадачності та вирішувати проблеми в стислі терміни

• Англійська мова розмовна на рівні спілкування з технічною підтримкою

   

Плюсом стане

• Досвід роботи з хмарними платформами (AWS, Azure, Google Cloud)
• Досвід роботи із системами Active Directory, Exchange Microsoft, Microsoft 365

• Досвід адміністрування баз даних PostgreSQL та MS SQL Server

   

Що ми пропонуємо

• Офіс або ремоут – вирішувати тобі. Ми даємо можливість працювати будь-де, а робоче місце облаштуємо
• Ремоут онбординг
• Перформанс бонуси для всіх (річні чи квартальні — залежить від ролі)
• Навчаємо працівників: є безліч внутрішніх ресурсів і програм від партнерів, власна бібліотека
• Страхування здоров’я і життя для працівників
• Wellbeing-програма та корпоративний психолог
• Компенсація витрат на мобільний зв'язок Київстар