Jobs

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

📌Vacancy: ML Engineer
📌Format: Remote

An international iGaming product company is looking for an ML Engineer who will create analytics for user segmentation, recommendations and risk identification based on big data.
The company already has a powerful database and resources - all that remains is to develop models and implement them in work processes 🚀

📌Expectations from the candidate:

Core Technical Skills

● Proficiency in Python for data science and ML (e.g., pandas, NumPy, scikit-learn, XGBoost, LightGBM, PyTorch).

● Strong SQL skills for deep-dive investigations and pipeline integrations.

● Experience with building production-grade ML pipelines using tools like Airflow, MLflow, Prefect, or similar.

● Skilled in feature engineering from raw logs or event streams (both user-level and transactional).

● Solid understanding of model evaluation (especially in high-imbalance, risk-heavy domains).

● Experience with data versioning and managing ML lifecycle in production (e.g., DVC, Feast, or custom solutions).

Data & Modeling Mindset

● Strong EDA (exploratory data analysis) skills and the ability to reason about data patterns, anomalies, and edge cases.

● Experience in binary classification, ranking, segmentation, and/or semi-supervised detection.

● Ability to design models and experiments that reflect real-world operational constraints and decision risks.

Bonus Points

● Background in fraud detection, risk scoring, or KYC/AML.

● Familiarity with real-time inference pipelines and streaming data processing (Kafka, Flink, Spark Streaming).

● Experience working in iGaming, payments, or financial services.

● Exposure to admin or ops tooling — model-driven UIs, analyst dashboards, or labeling workflows.

📌Key responsibilities:

● Data Exploration & Feature Engineering:

Dive into complex datasets - user activity, financial transactions, KYC logs - and extract meaningful patterns and risk signals.

Design robust and reusable feature sets for training, monitoring, and model

explainability.

● ML Model Development:

Build and evaluate models for fraud detection, user risk scoring, payment method ranking, and verification flow prediction.

Balance high class imbalance, cold-start edge cases, and evolving behavioral trends.

● Segmentation & Profiling:

Develop intelligent user clustering and segmentation frameworks based on

multi-dimensional activity and risk signals.

Enable downstream teams to personalize verification, UX, and promo logic.

● ML Pipeline & Deployment:

Create end-to-end pipelines: data preprocessing → feature stores → training → validation → batch/real-time inference.

Ensure model versioning, reproducibility, monitoring, and drift detection.

● Process Automation & Risk Tools:

Support automation of high-volume workflows (e.g., withdrawal reviews, user verifications) by integrating risk scores and confidence signals.

Help build internal tooling for analysts and risk teams to interact with model outputs and surface insights.

📌 The company offers:

● Remote work options;

● Managers interested in developing team members;

● 20 working days of vacation, 5 days off and sick pay;

● Work with large volumes of real behavioral data to solve complex risk-related problems.

● Help define how machine learning replaces manual operations in highly sensitive areas.

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’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.

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.

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 looking for a Data Scientist to support a Data & AI team. The role is focused on developing scalable AI/ML solutions and integrating Generative AI into evolving business operations.

About the Role

As a Data Scientist, you will:

• Collaborate with Product Owners, Data Analysts, Data Engineers, and ML Engineers to design, develop, deploy, and monitor scalable AI/ML products.
• Lead initiatives to integrate Generative AI into business processes.
• Work closely with business stakeholders to understand challenges and deliver tailored data-driven solutions.
• Monitor model performance and implement improvements.
• Apply best practices in data science and ML for sustainable, high-quality results.
• Develop and fine-tune models with a strong focus on accuracy and business value.
• Leverage cutting-edge technologies to drive innovation and efficiency.
• Stay updated on advancements in AI and data science, applying new techniques to ongoing processes.

About the Candidate

We are looking for a professional with strong analytical and technical expertise.

Must have:

• 3+ years of hands-on experience in Data Science and ML.
• Experience with recommendation systems and prescriptive analytics.
• Proficiency in Python, SQL, and ML libraries/frameworks.
• Proven experience developing ML models and applying statistical methods.
• Familiarity with containerization and orchestration tools.
• Excellent communication skills and strong command of English.
• Bachelor’s or Master’s degree in Computer Science, Statistics, Physics, or Mathematics.

Nice to have:

• Experience with Snowflake.
• Exposure to Generative AI and large language models.
• Knowledge of AWS services.
• Familiarity with NLP models (including transformers).

Job Description
 

About us
 

DevKit is more than just an IT company - we are a team of passionate professionals dedicated to creating cutting-edge solutions. Our employees work on exciting international startups, collaborating with highly skilled teams from around the world. With expertise in software development, design, and engineering, we help bring innovative ideas to life and drive success on a global scale!
 

About the project
 

Our project is a trailblazing AI-powered SaaS platform, reshaping the landscape of procurement from a tactical function to a strategic business ally that orchestrates seamless value chains. Our state-of-the-art solution empowers organizations to independently develop procurement strategies with a flexible framework adaptable to their specific needs and contexts.

Redefining traditional category management, our all-encompassing solution takes it to new heights by harnessing the capabilities of AI, fostering extensive collaboration, and integrating data from diverse sources beyond conventional category taxonomy. At our product we are driven by a purpose-centered ethos, dedicated to tackling ESG-related challenges, positioning procurement as a catalyst for positive change rather than merely a cost-cutting entity.

The multitude of prestigious awards and recognitions speaks to our leadership in the AI-driven procurement technology landscape, showcasing our unwavering commitment to innovation and excellence.

🛠 Desired Skills and Experience
 

• Have 5+ years of proven industry experience in applying Machine Learning and AI techniques.
• Hold a university degree, preferably a PhD, in relevant fields such as Artificial Intelligence, Machine Learning, Data Science, or Computer Science, or possess equivalent industrial experience.
• Demonstrate proficiency in Machine Learning principles and generative models, with an understanding of computational limitations and data privacy considerations.
• Showcase expertise in NLP/LLM, including prompt engineering, training, fine-tuning, RAG, evaluation, transfer learning techniques, and anomaly detection.
• Possess knowledge of Data Analysis/Processing, Data Visualization, and Storage systems (e.g., Databases, Data Lakes, Data Warehouses).
• Advocate for best coding practices, thorough documentation, rigorous reviews, data integrity, security, and performance optimization, with expertise in the Python ecosystem and popular ML libraries/frameworks, including those tailored for LLM. Have experience in Infrastructure management (MLOps/LLMOps, AWS/GCP Clouds, SQL, REST APIs, GitHub), and overseeing end-to-end ML pipelines.
• Previous software development experience and familiarity with our tech stack (React and Java) are advantageous.
• Demonstrate proficient English communication skills, both verbal and written, with a focus on clarity, openness, and professionalism.

🌟 Additional
 

• Embrace a remote-first work culture, offering flexibility to work from your preferred location within Berlin or nearby areas, either remotely or in a hybrid/onsite capacity at our centrally located office.
• Seize the opportunity to join the team in its early stages, making meaningful contributions to our engineering excellence within an international team and context.
• Occasional business travel within Europe may be required, averaging once per quarter, alongside team-building company events.
• Join a pioneering AI-driven procurement technology startup, contributing to the creation of innovative solutions that significantly impact enterprise customers.

🎯 Responsibilities

• Lead AI/DS/ML initiatives organization-wide, overseeing the entire ML lifecycle from conception to execution.
• Drive continuous development of AI/ML functionalities for our SaaS product, prioritizing well-structured, secure, and scalable code, complemented by comprehensive documentation and rigorous testing protocols.
• Initiate the integration of pertinent research findings into our product, particularly in procurement, through experimentation and the creation of solutions like chatbots, forecasts, and decision support systems.
• Utilize your software engineering expertise and business acumen to foster the growth and maintenance of our ML services, guiding all stages of technology development.
• Establish and manage high-quality datasets, alongside developing data pipelines for procurement data processing.
• Collaborate closely with cross-functional teams of software engineers to enact transformative changes within procurement practices.
• Stay informed about the latest advancements in AI/ML technologies and industry best practices, actively contributing insights for continuous improvement.
• Cultivate a culture of learning, knowledge sharing, and team development within our Data Science team.
   

Relocation to Berlin will be required after 6 months of work on the project!

In Competera, we are building a place where optimal pricing decisions can be made easily. We believe that AI technologies will soon drive all challenging decisions and are capable of helping humans be better.
We are now seeking a Junior Data Scientist to play a key role in reshaping the way we deliver our solutions.

What you will do

• Conduct Exploratory Data Analysis (EDA) to uncover hidden patterns and formulate hypotheses that shape the modeling strategy.
• Design and analyze A/B tests to measure the impact of your models and ideas.
• Train and evaluate predictive models, (feature engineering/ hyperparameter tuning), for challenges like demand forecasting and price elasticity estimation.
• Map business requirements into well-defined machine learning problems under consultancy.
• Communicate complex model outputs as clear, actionable insights for business stakeholders.
   

You have:

• SQL basics.
• A strong math background (Computer Science-related education is preferred).
• Scientific python toolkit (NumPy, pandas, scikit-learn, Keras / TensorFlow or PyTorch).
• Deep understanding of ML basics: overfitting, metrics, cross-validation, hyperparameter tuning, classification of ML tasks and models (classification, regression, clustering etc.).
• Good communication English skills (Intermediate+).
• 1+ year of hands-on experience in a data science.

Pleasant extras:

• Proven graduation from ML/AI MOOCs (Coursera, etc.).
• Participation in ML competitions (i.e. Kaggle).

Soft skills:

• Analytical mindset and critical thinking to solve complex problems.
• Agile approach, with the ability to experiment and test hypotheses in a dynamic business environment.
• Business-oriented thinking, capable of translating complex models into clear business insights.
• Curiosity and a drive for continuous learning in the data domain.
• Strong team player, able to collaborate across cross-functional teams.

You’re gonna love it, and here’s why:

• Rich innovative software stack, freedom to choose the best suitable technologies.
• Remote-first ideology: freedom to operate from the home office or any suitable coworking.
• Flexible working hours (we start from 8 to 11 am) and no time tracking systems on.
• Regular performance and compensation reviews.
• Recurrent 1-1s and measurable OKRs.
• In-depth onboarding with a clear success track.
• Competera covers 70% of your training/course fee.
• 20 vacation days, 15 days off, and up to one week of paid Christmas holidays.
• 20 business days of sick leave.
• Partial medical insurance coverage.
• We reimburse the cost of coworking.

Drive innovations with us. Be a Competerian.

Who we are:

Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries. 

About the Product:

Our client is a leading SaaS company offering pricing optimization solutions for e-commerce businesses. Its advanced technology utilizes big data, machine learning, and AI to assist customers in optimizing their pricing strategies and maximizing their profits.

About the Role:

As a Data Scientist you’ll play a critical role in shaping and enhancing our AI-driven pricing platform. 

Key Responsibilities:

• Develop and Optimize Advanced ML Models: Build, improve, and deploy machine learning and statistical models for forecasting demand, analyzing price elasticities, and recommending optimal pricing strategies.
• Lead End-to-End Data Science Projects: Own your projects fully, from conceptualization and experimentation through production deployment, monitoring, and iterative improvement.
• Innovate with Generative and Predictive AI Solutions: Leverage state-of-the-art generative and predictive modeling techniques to automate complex pricing scenarios and adapt to rapidly changing market dynamics.

Required Competence and Skills:

• A Master’s or PhD in Computer Science, Physics, Applied Mathematics or a related field, demonstrating a strong foundation in analytical thinking.
• At least 5 years of professional experience in end-to-end machine learning lifecycle (design, development, deployment, and monitoring).
• At least 5 years of professional experience with Python development, including OOP, writing production-grade code, testing, and optimization.
• At least 5 years of experience with data mining, statistical analysis, and effective data visualization techniques.
• Deep familiarity with modern ML/DL methods and frameworks (e.g., PyTorch, XGBoost, scikit-learn, statsmodels).
• Strong analytical skills combined with practical experience interpreting model outputs to drive business decisions.

Nice-to-Have:

• Practical knowledge of SQL and experience with large-scale data systems like Hadoop or Spark.
• Familiarity with MLOps tools and practices (CI/CD, model monitoring, data version control).
• Experience in reinforcement learning and Monte-Carlo methods.
• A solid grasp of microeconomic principles, including supply and demand dynamics, price elasticity, as well as econometrics.
• Experience with cloud services and platforms, preferably AWS.

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’re looking for an experienced engineer to join our team and work on building production-ready AI systems. This role is perfect for someone who enjoys combining AI/ML expertise with solid software engineering practices to deliver real-world solutions.

Requirements:

- AI/ML: 2+ years hands-on with LLM APIs, production deployment of at least one AI system

- Experience with LangChain, CrewAI, or AutoGen (one is enough)

- Understanding of prompt engineering (Chain-of-Thought, ReAct) and tool/function calling

- Python: 3+ years experience, strong fundamentals, Flask/FastAPI, async/await, REST APIs

- Production Experience: built systems running in production, handled logging, testing, error handling

- Cloud experience with AWS / GCP / Azure (one is enough)

- Familiar with Git, CI/CD, databases (PostgreSQL/MySQL)

Nice to Have:

Experience with vector databases (Pinecone, Weaviate)

Docker/containerization knowledge

Fintech or financial services background

Advanced ML/AI education or certifications

What You’ll Work On:

- Designing and deploying AI-powered systems using LLMs (OpenAI, Anthropic, etc.)

- Building agent-based solutions with frameworks like LangChain, CrewAI, or AutoGen

- Integrating AI systems with external APIs, databases, and production services

- Writing clean, tested Python code and deploying services to the cloud

- Collaborating with stakeholders to translate business requirements into technical solutions

Project

A system for automating accounting operations for companies, which reads, analyzes, compares, and interacts with accounting data. The goal is to make processes faster, more accurate, and scalable, minimize manual work, and increase client efficiency.

Project stage: MVP is nearly complete; the next step is to automate the MVP and scale the product.