Jobs

Ixilix is a technology-driven company that builds high-quality solutions and long-term partnerships. Our team is growing, and we are looking for a Game Mathematician.

Responsibilities:

• Writing rules for slots, showing the mechanics of the game, and documenting them in the Wiki;
• Creation of slots in accordance with the requirements of RTP, imposed by the rules (Main spins, Free games, Bonus games, features, etc.);
• Analyze and optimize game volatility and payout curves;
• Conducting a review of the rules of the slots checking the statistical data collected by bots when writing the server implementation of the game;
• Participation in general meetings that affect such things as the mechanics of slots;
• Actively participate in game planning sessions and propose innovative mechanics.

Required Skills:

• 3+ years of experience on positions like Mathematician in gambling is must;
• Degree in Mathematics, Statistics, or related fields (Bachelor’s, Master’s, or Ph.D.);
• Understanding of applied mathematics: probability theory and statistics, numerical methods, linear algebra, optimization;
• Ability to describe processes in detail and clearly articulate their thoughts in writing;
• Analytical approach in the process of working on problems;
• Attention to detail, generation of ideas.
• English B2+.
• Ukrainian C1+.

Preferred Skills:

• Knowledge of algorithms and data structures, interpolation methods, regression models, theory of stochastic processes, cryptography;

What we offer:

Rewards & Celebrations 

• Quarterly Bonus System
• Team Buildings Compensations
• Memorable Days Financial Benefit

Learning & Development

• Annual fixed budget for personal learning 
• English Language Courses Compensation

Time Off & Leave

• Paid Annual Leave (Vacation) - 24 working days
• Sick leave - unlimited number of days, fully covered

Wellbeing Support

• Mental Health Support (Therapy Compensation)
• Holiday Helper Service

Workplace Tools & Assistance

• Laptop provided by Company (after probation)

Work conditions:

• Remote work from EU
• Flexible 8-hour workday, typically between 9:00 - 18:00 CET
• Five working days, Monday to Friday
• Public holidays observed according to Ukrainian legislation
• Business trips to Bratislava every 3-6 months (company provides compensation of expenses)

At Ixilix, we value transparency, trust, and ownership. We believe that great results come from people who care - about their work, their team, and the impact they create. 

Sounds like you? Let’s connect! We’re just one click away.

Responsibilities:

• Design data science, statistical, machine learning and deep learning systems that influence millions of players
• Implement and optimize appropriate ML algorithms and tools for time series and tabular data
• Transform data science prototypes into full-scale products, while deploying and monitoring ML models
• Run live tests and experiments
• Train and retrain systems when necessary
• Create or extend existing ML libraries and frameworks
• Collaborate with other scientists, engineers, architects and analysts spread across several countries

Requirements:

• MSc in Computer Science, or any related degree
• Solid working experience in Python and Java - high coding standards, clean code, well documented, and extensive unit testing- Must.
• Experience working with databases (SQL and no-SQL)
• Experience with Scala
• Experience with training, testing, deployment, and monitoring real-time (or near real-time) machine learning models in production
• Experience with machine learning frameworks (like Keras, Tensorflow, or PyTorch) and libraries (like scikit-learn) - Big Advantage. 
• Experience with Big Data tools, in particular batch and stream processing (Spark, Kafka, Hadoop, Hive, etc.) - Must 
• Good understanding of container & orchestration technologies (Docker, Kubernetes, etc.) - Must
• Experience working on high-scale, production-grade projects
• All-around team player who is a self-motivated, fast learner

Location: UK/EU / Onsite / Hybrid 3 out of 5 / Remote (only for exceptional candidate) 

Department: Core Engineering

Seniority Level: Principal / Lead

Industry: edge AI, embedded systems, dual-use systems (industrial IoT/ security/ defence /cyber tech)

About the Role

We’re hiring a Principal Embedded Systems Engineer to lead the design, optimization, and deployment of our embedded computer vision platform based on NVIDIA Jetson. This system integrates multiple cameras, edge AI models, and other peripherals in mission-critical environments. You will architect, build, and optimize the entire software stack — from Linux kernel to AI inference — and own key technical decisions around system performance, thermal optimization, and hardware compatibility.

Core Responsibilities

• Lead system architecture and embedded design across the Jetson platform (Orin/Nano/Xavier)
• Build and optimize Linux systems using Yocto Project and/or Buildroot
• Set up and tune ISP pipelines, handle multiple cameras via V4L2, and manage streaming pipelines via GStreamer
• Handle kernel configuration and performance optimization
• Lead system profiling and thermal optimization efforts across CPU, GPU, and memory
• Run hardware compatibility tests for selecting cameras, interfaces, power modules, etc.
• Deploy and run AI models on-device using tools like TensorRT, CUDA, and cuDNN
• Work with communication protocols like UART and MAVLink for integration with drones/UAVs
• Contribute to internal documentation, architecture specs, and build-test workflows
• Act as a proactive technical leader, challenging and owning solutions end-to-end

You’ll Thrive Here If You Have
Must-Have Technical Skills

• 5+ years working with embedded Linux systems (preferably with real deployments)
• Deep experience with Yocto Project or Buildroot
• Expertise in Jetson platforms (Nano, Orin NX, AGX) and JetPack SDK
• Strong C++ and Python skills for low-level and integration development
• Comfort with performance profiling tools and debugging embedded bottlenecks

• Familiarity with camera systems, ISP tuning, and Linux video stacks (V4L2)

   

Strong Plus

• Experience with GStreamer pipelines on embedded devices
• Hands-on with CUDA, TensorRT, cuDNN or other NVIDIA edge inference stacks
• Exposure to Balena, Mender, or other fleet/update management tools
• Understanding of UAV/drones systems, especially UART, MAVLink, or similar industrial communication protocols
• Prior exposure to robotics, or low-latency compute environments
• Experience integrating AI models on-device for real-time tasks

Collaboration & Mindset

• Excellent English and communication skills; ability to write specs and lead discussions
• Product ownership mindset — we’re looking for someone who owns decisions, not just executes tickets
• Ability to challenge solution architectures with well-structured, testable alternatives
• Collaborative mindset to work with AI, hardware, and firmware teams

Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses.

We are seeking a highly skilled Machine Learning (ML) Tech Lead with a strong background in Large Language Models (LLMs) and AWS Cloud services. The ideal candidate will oversee the development and deployment of cutting-edge AI solutions while managing a team of 5-10 engineers. This leadership role demands hands-on technical expertise, strategic planning, and team management capabilities to deliver innovative products at scale.

Responsibilities:

• Leadership & Management

-Lead and manage a team of 5-10 engineers, providing mentorship and fostering a collaborative team environment;

-Drive the roadmap for machine learning projects aligned with business goals;

-Coordinate cross-functional efforts with product, data, and engineering teams to ensure seamless delivery.

• Machine Learning & LLM Expertise

-Design, develop, and fine-tune LLMs and other machine learning models to solve business problems;

-Evaluate and implement state-of-the-art LLM techniques for NLP tasks such as text generation, summarization, and entity extraction;

-Stay ahead of advancements in LLMs and apply emerging technologies;

-Expertise in multiple main fields of ML: NLP, Computer Vision, RL, deep learning and classical ML.

• AWS Cloud Expertise

-Architect and manage scalable ML solutions using AWS services (e.g., SageMaker, Lambda, Bedrock, S3, ECS, ECR, etc.);

-Optimize models and data pipelines for performance, scalability, and cost-efficiency in AWS;

-Ensure best practices in security, monitoring, and compliance within the cloud infrastructure.

• Technical Execution

-Oversee the entire ML lifecycle, from research and experimentation to production and maintenance;

-Implement MLOps and LLMOps practices to streamline model deployment and CI/CD workflows;

-Debug, troubleshoot, and optimize production ML models for performance.

• Team Development & Communication

-Conduct regular code reviews and ensure engineering standards are upheld;

-Facilitate professional growth and learning for the team through continuous feedback and guidance;

-Communicate progress, challenges, and solutions to stakeholders and senior leadership.

Qualifications:

• Proven experience with LLMs and NLP frameworks (e.g., Hugging Face, OpenAI, or Anthropic models);
• Strong expertise in AWS Cloud Services;
• Strong experience in ML/AI, including at least 2 years in a leadership role;
• Hands-on experience with Python, TensorFlow/PyTorch, and model optimization;
• Familiarity with MLOps tools and best practices;
• Excellent problem-solving and decision-making abilities;
• Strong communication skills and the ability to lead cross-functional teams;
• Passion for mentoring and developing engineers.

We are looking for a Computer Vision Engineer with an expertise in low-level CV algorithms.

Required Qualifications: 

• 5+ years of experience in computer vision
• Expert-level in Python
• Proficiency in C++
• Extensive experience in DL and PyTorch framework for CV stacks
• Understanding of geometrical computer vision principles
• Model optimization: quantization, pruning, nn compilers
• Hands-on experience in implementing low-level CV algorithms

Nice to Have:

• Experience with DL on edge devices
• Experience with SLAM and/or Visual-Inertial Odometry (VIO)
• Experience with sensor fusion (IMU, magnetometer, GNSS, camera)
• Familiarity with Kalman filters

Our client is a Spain-based company and one of the prominent providers of unique online slot games. With a team of 20+ developers, designers, and marketers, they are committed to building innovative and captivating slot experiences for a global audience.

About the Role

The team is now looking for a Middle Python/ML Developer to join the development of an AI-driven platform for automating slot game creation — from processing Game Design Documents (GDDs) to generating production-ready code. The role combines backend engineering with applied AI/LLM challenges.

🔧 Responsibilities

• Develop and enhance backend services (FastAPI, async SQLAlchemy, PostgreSQL).
• Implement and optimize RAG pipelines and vector search (pgvector).
• Build integrations with Confluence, Jira, RocketChat; async messaging via RabbitMQ.
• Scale multi-agent workflows, improve performance and reliability.
• Set up monitoring, logging, and CI/CD pipelines.
• Prepare infrastructure for TypeScript code generation.
• Validate and generate artifacts from Game Design Documents (GDD).

✅ Requirements

• 3+ years of professional Python development.
• Strong in Python 3.10+ (async/await, type hints), FastAPI (production).
• Async SQLAlchemy 2.0+, PostgreSQL (indexes, performance tuning).
• Docker (multi-stage builds, docker-compose).
• Solid background in async Python (asyncio, aio-libs).
   

🌟 Nice-to-Have

• Experience with pgvector, semantic search, RAG.
• LLM integration (OpenAI/Ollama), prompt engineering.
• RabbitMQ/Redis, event-driven architectures.
• Basic TypeScript knowledge.

• Background in gamedev or document/content systems.

   

Сompanу offer:

• The opportunity to play a key role in developing new team processes.
• Freedom for experiments and research that influence decision-making.
• Mentorship from experienced specialists, as well as collaboration in a creative and supportive team.
• Participation in projects where you can implement cutting-edge technologies and see their real impact.
• Access to modern tools and resources for learning and professional growth.
• Flexible schedule and the possibility to work remotely.

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
Our client is an IT company that develops technological solutions and products to help companies reach their full potential and meet the needs of their users. The team comprises over 600 specialists in IT and Digital, with solid expertise in various technology stacks necessary for creating complex solutions.

About the role:
We are looking for 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 developing model training pipelines with other talented data scientists. Your work will directly shape the quality and capabilities of the models by ensuring we feed them the highest-quality, most relevant data possible.

Requirements:
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.).
- 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.
- 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.
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 clearly.
- Ability to rapidly prototype and iterate on ideas

Nice to have:
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 similar processing pipelines 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) indicating 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 text sources and data sets, or experience with multilingual data processing, can be an advantage given the 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.

Responsibilities:
- 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.

The company offers:
- Competitive salary.
- Equity options in a fast-growing AI company.
- Remote-friendly work culture.
- Opportunity to shape a product at the intersection of AI and human productivity.
- Work with a passionate, senior team building cutting-edge tech for real-world business use.

WE ARE

SoftServe is a global digital solutions company, headquartered in Austin, Texas, and founded in 1993.

With 2,000+ active projects across the USA, Europe, APAC, and LATAM, we deliver meaningful outcomes through bold thinking and deep expertise. Our people create impactful solutions, drive innovation, and genuinely enjoy what they do.

The AI and Data Science Center of Excellence (CoE) is SoftServe’s premier AI/ML hub, primarily based in Europe. With 130+ experts—including data scientists, research analysts, MLOps engineers, ML and LLM architects — we cover the full AI lifecycle, from problem framing to deployment.

In 2024, we delivered 150+ AI projects, including over 100 focused on Generative AI, combining scale with measurable impact.

We are a 2024 NVIDIA Service Delivery Partner and maintain strong collaborations with Google Cloud, Amazon, and Microsoft, ensuring our teams always work with cutting-edge tools and technologies.

We also lead Gen AI Lab — our internal innovation engine focused on applied research and cross-functional collaboration in Generative AI.

In 2025, a key area of innovation is Agentic AI — where we design and deploy autonomous, collaborative agent systems capable of addressing complex, real-world challenges at scale for our clients and internally.

IF YOU ARE

• A holder of a Ph.D. or master’s degree in Computer Science or a related field
• Experienced in Generative AI and natural language processing (NLP), working with large-scale transformer models and generative pre-trained LLMs like GPT-4, Claude, and Gemini
• Knowledgeable about the latest advancements in diffusion models and other generative frameworks for text and image generation
• Adept at applying advanced deep learning techniques to practical use cases
• Well-versed in emerging trends and breakthroughs in machine learning, deep learning, and NLP, with a strong focus on their real-world applications
• Proficient in working with state-of-the-art pre-trained language models like GPT-4 and BERT, including fine-tuning for specialized tasks
• Aware of the software development lifecycle for AI projects and the operationalization of machine learning models
• Experienced in deploying AI solutions on major cloud platforms
• Hands-on with Python and deep learning frameworks such as TensorFlow or PyTorch
• Skilled in interpersonal communication, analytical reasoning, and complex problem-solving
• Capable of translating technical concepts into clear, concise insights that non-technical audiences can easily grasp
• Proficient in business communication in English at an upper-intermediate level
   

AND YOU WANT TO

• Work with the full stack of data analysis, deep learning, and machine learning model pipeline that includes deep analysis of customer data, modeling, and deployment in production
• Choose relevant computational tools for study, experiment, or trial research objectives
• Drive the development of innovative solutions for language generation, text synthesis, and creative content generation using the latest state-of-the-art techniques
• Develop and implement advanced Generative AI solutions such as intelligent assistants, Retrieval-Augmented Generation (RAG) systems, and other innovative applications
• Produce clear, concise, well-organized, and error-free computer programs with the appropriate technological stack
• Present results directly to stakeholders and gather business requirements
• Develop expertise in state-of-the-art Generative AI techniques and methodologies
• Grow your skill set within a dynamic and supportive environment
• Work with Big Data solutions and advanced data tools in cloud platforms
• Build and operationalize ML models, including data manipulation, experiment design, developing analysis plans, and generating insights
• Lead teams of data scientists and software engineers to successful project execution

TOGETHER WE WILL

• Be part of a team that's shaping the future of AI and data science through innovation and shared growth.
• Advance the frontier of Agentic AI by shaping intelligent multi-agent ecosystems that drive autonomy, scalability, and measurable business value.
• Have access to world-class training, cutting-edge research, and collaborate with top industry partners.
• Maintain a synergy of Data Scientists, DevOps team, and ML Engineers to build infrastructure, set up processes, productize machine learning pipelines, and integrate them into existing business environments
• Communicate with the world-leading companies from our logos portfolio
• Enjoy the opportunity to work with the latest modern tools and technologies on various projects
• Participate in international events and get certifications in cutting-edge technologies
• Have access to powerful educational and mentorship programs
• Revolutionize the software industry and drive innovation in adaptive self-learning technologies by leveraging multidisciplinary expertise

United Tech is a global IT product company shaping the future of real-time social connection.

With millions of users across North America, Europe, LATAM, and MENA, we build next-gen mobile and web apps for live-streaming and social networking.

Our platforms enable connection at scale fast, interactive, and deeply engaging.

The market is projected to exceed $206B by 2030, and we are already leading the evolution.

Founded in Ukraine, scaling worldwide. Are you in?

About the role: This is a role for a leader who thrives in a fast-moving product environment, where challenges fuel growth and every decision shapes the future. With United Tech you will have the freedom to design, build, and re-engineer processes from the ground up, working side by side with a team that values initiative, knowledge sharing, and bold goals. Your ideas will directly influence revenue, and you will see the tangible results of your work reflected in key financial metrics. We move fast, we cut through bureaucracy, and we take on complex challenges that push both personal and professional boundaries, limited only by the scale of your ambitions

In this role, you will

• Build, scale, and develop the Data Science team, including hiring, mentoring, and performance evaluation
• Define and execute a Data Science strategy aligned with business priorities
• Oversee the full lifecycle of DS projects from problem formulation to deploying models into production
• Prioritize initiatives based on business impact (ROI, time-to-market)
• Collaborate closely with product managers, analysts, engineers, and C-level executives

It’s all about you

• Proven ability to implement best practices in Data Science: reproducibility, A/B testing, ML monitoring
• Strong track record in maintaining model quality (performance, drift, latency)
• Advanced Python skills (pandas, sklearn, numpy, xgboost; pytorch/tf is a plus)
• High-level SQL expertise (large datasets, query optimization)
  Hands-on experience with ML pipelines, orchestration (Airflow, Prefect), and monitoring (Evidently, MLflow, Prometheus)
• Experience with GCP
• Solid understanding of A/B testing, causal inference, and statistics
• Familiarity with architecture fundamentals (API, data pipelines, microservices — integration level)
• Cloud experience with AWS or Azure
• Knowledge of distributed computation tools (e.g., Spark, CloudRun)
• Proven track record with generative AI/NLP model deployment
• Experience in startups or high-growth companies
• Publications, conference speaking, achievements on Kaggle, or open-source contributions

Would be a plus

• Experience optimizing company processes through AI solutions
• Implementation of AI tools across departments (development, support, marketing, etc.)

What we offer

Care and support: 

• 20 paid vacation days, 15 sick days, and 6 additional days off for family events
• Up to 10 additional days off for public holidays
• 100% medical insurance coverage
• Sports and equipment reimbursement
• Team building events, corporate gifts, and stylish merch
• Financial and legal support
• Position retention and support for those who join the Armed Forces of Ukraine
• Participation in social initiatives supporting Ukraine
   

Comfortable working environment:

• Work from our Kyiv hub or remotely with a flexible schedule 
• Workspace rental reimbursement in other cities and abroad
• Modern equipment or depreciation of your own tools
   

Investment in your future:

• Collaborate with a highly-skilled team of Middle & Senior professionals, sharing practical cases and expertise in the social networking niche
• 70% of our heads and leads have grown into their roles here – so can you!
• Performance-oriented reviews and Individual Development Plans (IDPs)
• Reimbursement for professional courses and English classes
• Corporate library, book club, and knowledge-sharing events
   

Hiring process

• Intro call
• Technical Interview
• Interview with Hiring Manager
• Final Interview
• Reference check
• Offer

Join us at Provectus to be a part of a team that is dedicated to building cutting-edge technology solutions that have a positive impact on society. Our company specializes in AI and ML technologies, cloud services, and data engineering, and we take pride in our ability to innovate and push the boundaries of what's possible.

As an ML Engineer, you’ll be provided with all opportunities for development and growth.

Let's work together to build a better future for everyone!
 

Requirements:

• Comfortable with standard ML algorithms and underlying math.
• Strong hands-on experience with LLMs in production, RAG architecture, and agentic systems
• AWS Bedrock experience strongly preferred
• Practical experience with solving classification and regression tasks in general, feature engineering.
• Practical experience with ML models in production.
• Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines.
• Solid software engineering skills (i.e., ability to produce well-structured modules, not only notebook scripts).
• Python expertise, Docker.
• English level - strong Intermediate.
• Excellent communication and problem-solving skills.
   

Will be a plus:

• Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda).
• Practical experience with deep learning models.
• Experience with taxonomies or ontologies.
• Practical experience with machine learning pipelines to orchestrate complicated workflows.
• Practical experience with Spark/Dask, Great Expectations.
   

Responsibilities:

• Create ML models from scratch or improve existing models. 
• Collaborate with the engineering team, data scientists, and product managers on production models.
• Develop experimentation roadmap. 
• Set up a reproducible experimentation environment and maintain experimentation pipelines.
• Monitor and maintain ML models in production to ensure optimal performance.
• Write clear and comprehensive documentation for ML models, processes, and pipelines.
• Stay updated with the latest developments in ML and AI and propose innovative solutions.

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’re looking for a skilled Data Scientist with a focus on AI data to power the next generation of intelligent solutions. This role is perfect for someone who thrives on tackling complex challenges through machine learning, advanced analytics, and coding, and who’s eager to drive innovation through cutting-edge AI methodologies.

Responsibilities

• Semantic Modeling: Design and maintain semantic representations (e.g., ontologies, entity relationships) to enhance structured data queryability and AI-driven reasoning
• Natural Language to Structured Query Mapping: Design and evaluate approaches that interpret natural language questions and accurately map them to structured data queries (e.g., SQL or semantic equivalents)
• Data-to-Text Interpretation: Design and evaluate techniques for interpreting tabular data and generating human-readable natural language explanations
• LLM Prompt Strategy: Assist in developing and refining prompt engineering strategies to ensure accurate, interpretable, and relevant responses from large language models
• Model Evaluation and Enhancement: Continuously evaluate, refine, and improve existing AI models and algorithms to enhance accuracy, scalability, and computational efficiency
• Research & Innovation: Stay current on advancements in AI, GenAI, NLP, and machine learning, incorporating promising techniques and ideas into product solutions
  
  Requirements
• Master’s degree in Computer Science, Data Science or Applied Mathematics is required; a Ph.D. is strongly preferred
• 5+ years of experience in Data Science field
• Proficiency in Python and common AI/ML libraries (e.g., PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, Langchain, Langgraph)
• Strong MLOps experience to help build, deploy, and maintain machine learning models in production. Familiarity with cloud platforms (e.g., AWS, GCP, Azure)
• Experience with Agentic system design
• Focus on researcher experience in writing articles
  
  What we offer
• Competitive salary and benefits package
• Medical insurance
• Full Remote
• Collaborative and innovative work environment
• Career growth and development opportunities

• A chance to work with a talented and driven team of professionals
  
  About the project

  Our client develops a unique in-memory platform using innovative Machine Learning technologies. The product aims to help businesses’ achieve data and analytics processing needs with the highest speed, and to deliver real-time performance by reproducing companies’ data to the in-memory data store. An impressively fast-growing company that partners with the most leading enterprises from all over the world within various industries including healthcare, telecommunications, retail, etc.

ABOUT CLIENT

An international organization with over 20 years of experience in advancing professional medical education and improving patient care. It focuses on evidence-based medicine and delivers educational programs in oncology, cardiology, and women’s health, reaching a global audience of healthcare professionals.
 

PROJECT TECH STACK

MS PowerBI, QLIK, MS Office (Excel, PowerPoint, Docs)
 

PROJECT STAGE

Live product
 

QUALIFICATIONS AND SKILLS

• Data Engineering Background: 5+ years of experience in data engineering, with a strong understanding of data pipelines, architectures, and tools (e.g. Apache Beam, Apache Spark, AWS Glue);
• Machine Learning Experience: 3+ years of experience in machine learning engineering and/or data science, with a focus on traditional AI techniques;
• Strong programming skills in Python, with experience working with machine learning libraries and frameworks;
• Experience working with graph databases and querying languages (e.g. Cypher, SPARQL);
• Familiarity with SageMaker and/or other cloud-based machine learning platforms;
• Strong understanding of machine learning algorithms and techniques, including supervised and unsupervised learning;
• Ability to make informed decisions about model selection, deployment, and maintenance;
• Experience working on large-scale products and data science projects.
   

NICE TO HAVE

• Experience with generative AI techniques, such as RAG optimization and agentic workflows;
• PhD in Computer Science, Statistics, or related field (not required, but a huge plus);
• Experience working with large-scale datasets and distributed computing environments;
• Background in Ad tech and marketing tech.
   

RESPONSIBILITIES

• Optimize machine learning models – primarily using traditional AI techniques (approx. 80%) with selective application of generative AI solutions (approx. 20%);
• Leverage AWS SageMaker and other AWS services to build, refine, and deploy ML models, improving existing implementations without starting from scratch;
• Analyze large, multi-platform marketing and advertising datasets, translating results into actionable insights for a unified MarTech/AdTech backend platform;
• Collaborate closely with data engineering, backend development, and product teams to integrate algorithms into production workflows, ensuring scalability and performance;
• Strategize and recommend optimal AI/ML approaches without bias toward a specific technology, balancing innovation with practical application;
• Support automation initiatives in marketing analytics, contributing to feature engineering, workflow optimization, and integration of agentic AI components

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.

About the client:
Our client is an IT company that develops technological solutions and products to help companies reach their full potential and meet the needs of their users. The team comprises over 600 specialists in IT and Digital, with solid expertise in various technology stacks necessary for creating complex solutions.

About the role:
We are looking for 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 developing model training pipelines with other talented data scientists. Your work will directly shape the quality and capabilities of the models by ensuring we feed them the highest-quality, most relevant data possible.

Requirements:
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.).
- 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.
- 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.
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 clearly.
- Ability to rapidly prototype and iterate on ideas

Nice to have:
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 similar processing pipelines 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) indicating 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 text sources and data sets, or experience with multilingual data processing, can be an advantage given the 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.

Responsibilities:
- 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.

The company offers:
- Competitive salary.
- Equity options in a fast-growing AI company.
- Remote-friendly work culture.
- Opportunity to shape a product at the intersection of AI and human productivity.
- Work with a passionate, senior team building cutting-edge tech for real-world business use.