Jobs Data Science

Azzurro.io is looking for ML Engineer.

Location: Remote (Company based in Seattle)

About the Client: Our client partners with brands to improve their sales and visibility on Amazon through optimized advertising campaigns and keyword strategies. Using data-driven insights, they help clients increase their impact in the marketplace.

Role Overview: As a Data Scientist, you'll build predictive models using time series data (e.g., product ranks, keyword performance, ad campaigns) to help optimize our client’s Amazon strategies. You’ll analyze, clean, and transform data to train ML models that improve ad performance and drive sales.

Responsibilities:

• Predictive Modeling: Develop and refine models to analyze product rankings and campaign performance.
• Data Preparation: Extract, clean, and transform large datasets for analysis and modeling.
• Regression Analysis: Build regression models to forecast trends and improve keyword strategies.
• Model Optimization: Use techniques like feature engineering and hyperparameter tuning to improve model performance.

Tech Stack:

• Core Technologies: AWS, MySQL, Python.
• Data & ML Tools: Python libraries (NumPy, pandas), frameworks (e.g., TensorFlow or similar).

Qualifications:

• Strong background in analytics, mathematics, and experience with time series data.
• Proficiency in Python, including data libraries (NumPy, pandas) and ML frameworks (e.g., TensorFlow, PyTorch).
• Experience building regression models and optimizing predictive algorithms.
• Working knowledge of SQL for querying large datasets.
  
  P.S. please pay your attention that this position is open in Azzurro.io company, not Pics.io.

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.

Responsibilities

Model Fine-Tuning and Deployment:

Fine-tune pre-trained models (e.g., BERT, GPT) for specific tasks and deploy them using Amazon SageMaker and Bedrock.

RAG Workflows:

Establish Retrieval-Augmented Generation (RAG) workflows that leverage knowledge bases built on Kendra or OpenSearch. This includes integrating various data sources, such as corporate documents, inspection checklists, and real-time external data feeds.

MLOps Integration:

The project includes a comprehensive MLOps framework to manage the end-to-end lifecycle of machine learning models. This includes continuous integration and delivery (CI/CD) pipelines for model training, versioning, deployment, and monitoring. Automated workflows ensure that models are kept up-to-date with the latest data and are optimized for performance in production environments.

Scalable and Customizable Solutions:

Ensure that both the template and ingestion pipelines are scalable, allowing for adjustments to meet specific customer needs and environments. This involves setting up RAG workflows, knowledge bases using Kendra/OpenSearch, and seamless integration with customer data sources.

End-to-End Workflow Automation:

Automate the end-to-end process from user input to response generation, ensuring that the solution leverages AWS services like Bedrock Agents, CloudWatch, and QuickSight for real-time monitoring and analytics.

Advanced Monitoring and Analytics:

Integrated with AWS CloudWatch, QuickSight, and other monitoring tools, the accelerator provides real-time insights into performance metrics, user interactions, and system health. This allows for continuous optimization of service delivery and rapid identification of any issues.

Model Monitoring and Maintenance:

Implement model monitoring to track performance metrics and trigger retraining as necessary.

Collaboration:

Work closely with data engineers and DevOps engineers to ensure seamless integration of models into the production pipeline.

Documentation:

Document model development processes, deployment procedures, and monitoring setups for knowledge sharing and future reference.

Must-Have Skills

Machine Learning: Strong experience with machine learning frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.

MLOps Tools: Proficiency with Amazon SageMaker for model training, deployment, and monitoring.

Document processing: Experience with document processing for Word, PDF, images.

OCR: Experience with OCR tools like Tesseract / AWS Textract (preferred)

Programming: Proficiency in Python, including libraries such as Pandas, NumPy, and Scikit-Learn.

Model Deployment: Experience with deploying and managing machine learning models in production environments.

Version Control: Familiarity with version control systems like Git.

Automation: Experience with automating ML workflows using tools like AWS Step Functions or Apache Airflow.

Agile Methodologies: Experience working in Agile environments using tools like Jira and Confluence.

Nice-to-Have Skills

LLM: Experience with LLM / GenAI models, LLM Services (Bedrock or OpenAI), LLM abstraction like (Dify, Langchain, FlowiseAI), agent frameworks, rag.

Deep Learning: Experience with deep learning models and techniques.

Data Engineering: Basic understanding of data pipelines and ETL processes.

Containerization: Experience with Docker and Kubernetes (EKS).

Serverless Architectures: Experience with AWS Lambda and Step Functions.

Rule engine frameworks: Like Drools or similar

If you are a motivated individual with a passion for ML and a desire to contribute to a dynamic team environment, we encourage you to apply for this exciting opportunity. Join us in shaping the future of infrastructure and driving innovation in software delivery processes.

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.

About Forecasa

Forecasa is a profitable, founder‑led SaaS company that turns raw real‑estate transaction data into decision‑grade intelligence for hedge funds, private‑lenders, and MBS desks. We move fast, value autonomy with accountability, and maintain a culture where clear documentation beats hierarchy.

What you’ll do

• Engineer risk‑focused features (borrower, lender, property, geography)  in Python/PySpark.
• Develop and validate PD / LGD models using WoE, IV, logistic GBM, XGBoost, or similar.
• Prototype lender‑health metrics (capital‑diversification, portfolio turnover, market concentration, etc.) for client dashboards.
• Create robust, reproducible data pipelines (git‑versioned, unit‑tested, CI in GitLab).

• Produce concise notebooks & dashboards that can feed automated PDF reports.

   

Must‑have qualifications

• 4 – 6+ years in data science, risk analytics, or credit‑modeling.
• Strong Python (pandas, NumPy, scikit‑learn) and SQL; solid PySpark on distributed data a big plus.
• Hands‑on experience building or validating credit‑risk or fraud models (PD, scorecards, Basel/IFRS 9, etc.).
• Fluency in statistics (inferential tests, multicollinearity, model monitoring).
• Git workflow, code review discipline, and comfort with Agile/Kanban boards.
• Clear written & spoken English; able to summarize findings for non‑technical stakeholders.

Nice‑to‑haves

• Familiarity with U.S. mortgage or private‑lending data.
• Experience with Postgres, MinIO/S3, or dbt.
• Knowledge of BI/visualization tools (Plotly, PowerBI, Looker, etc).
• Prior work in a fully remote, internationally‑distributed team.

How we work

• Stack: Python • PySpark • PostgreSQL/Snowflake • GitLab CI • AWS & on‑prem Spark
• Communication: Slack, Zoom, Notion. Meetings kept lean; deliverables drive the schedule.
• Culture: Low‑ego, high‑ownership. We favor clarity, rapid feedback loops, and well‑documented processes.

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.

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.

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 looking for a Computer Vision Engineer with a background in classical computer vision techniques and hands-on implementation of low-level CV algorithms.

The ideal candidate will have experience with SLAM, Visual-Inertial Odometry (VIO), and sensor fusion.

We consider engineers at Middle/Senior levels — tasks and responsibilities will be adjusted accordingly.

Required Qualifications:

• 3+ years of hands-on experience with classical computer vision
• Knowledge of popular computer vision networks and components 
• Understanding of geometrical computer vision principles
• Hands-on experience in implementing low-level CV algorithms
• Practical experience with SLAM and/or Visual-Inertial Odometry (VIO)
• Proficiency in C++
• Experience with Linux
• Ability to quickly navigate through recent research and trends in computer vision.
• Relevant work experience or education in STEM field

Nice to Have:

• Experience with Python
• Familiarity with neural networks and common CV frameworks/libraries (OpenCV, NumPy, PyTorch, ONNX, Eigen, etc.)
• Experience with sensor fusion.

PwC is a network of over 370,000 employees in 149 countries focused on providing the highest quality services in the areas of audit, tax advisory, consulting and technology development.

What we offer:
- Official employment;
- Remote work opportunity;
- Annual performance and grade review;
- A Dream team of experienced colleagues and high-class specialists;
- Language courses (English & Polish languages);
- Soft skills development;
- Personal development plan and career coach;
- Corporate events and team-buildings.

Main responsibilities:

• Developing innovative solutions for our clients by leveraging cutting-edge data science, machine learning, and AI technologies;
• Developing intelligent assistants using the latest large language models (e.g., GPT-4, Falcon 2, LLAMA 3, Mixtral), employing Retrieval Augmented Generation techniques, and utilizing agent frameworks (e.g., Langraph, CrewAI);
• Utilizing AI expertise to recommend the most effective technical approaches and solution architectures for addressing business challenges;
• Leading data science project teams of 1-5 members, managing small to medium projects, and overseeing parts of larger engagements under senior supervision;
• Working closely with PwC industry experts, clients, and higher management while actively participating in the proposal-making process within your area of expertise;
• Communicating complex insights in a clear and actionable manner to non-technical colleagues and clients.

Requirements:

• 3+ years of relevant professional experience;
• Solid knowledge of ML/AI concepts: types of algorithms, machine learning frameworks, model efficiency metrics, model life-cycle, AI architectures;
• Knowledge and experience in production grade code development in Python;
• Solid knowledge of SQL;
• Experience with LLMs and related concepts (e.g. RAG, vector DBs, AI agents);
• Understanding of cloud concepts and architectures, with hands-on experience in cloud services (GCP, AWS, Azure);
• Knowledge of CI/CD and DevOps practices;
• Experience with deploying code with Docker / Kubernetes;
• Strong interpersonal and communication skills — essential in day-to-day cooperation with clients and the team;
• Outstanding supervision and mentorship abilities;
• Graduate of Economics, Econometrics, Quantitative Methods, Computer Science, Math, Physics, Operational Research or related discipline;
• Excellent analytical and problem-solving skills, including the ability to independently disaggregate issues, identify root causes and recommend solutions to business problems;

• Proficiency in English, both written and spoken.

   

  Nice to have: 

• Familiarity with MLOps tools (e.g., Azure AI Studio, AzureML, Vertex.AI, SageMaker, MLFlow);
• Knowledge of an extra programming language (e.g. C#, Go, Java);
• Knowledge of Natural Language Processing techniques;
• Experience in banking, retail or consulting;
• Experience in leading project teams.

Why PwC?

We are not just numbers and reports. PwC is the impact you can create through your actions. Our team will help you achieve more, and we are ready to start this journey with you.

Ready for a challenge? Send your resume and join the team that is shaping the future!

Quantum is a global technology partner delivering high-end software products that address real-world problems.

We advance emerging technologies for outside-the-box solutions. We focus on Machine Learning, Computer Vision, Deep Learning, GIS, MLOps, Blockchain, and more.

Here at Quantum, we are dedicated to creating state-of-art solutions that effectively address the pressing issues faced by businesses and the world. To date, our team of exceptional people has already helped many organizations globally attain technological leadership.

We constantly discover new ways to solve never-ending business challenges by adopting new technologies, even when there isn’t yet a best practice. If you share our passion for problem-solving and making an impact, join us and enjoy getting to know our wealth of experience!

About the position

Quantum is expanding the team and has brilliant opportunities for a Data Science Engineer. The client is a technological research company that utilizes proprietary AI-based analysis and language models to provide comprehensive insights into global stocks in all languages. Our mission is to bridge the knowledge gap in the investment world and empower investors of all types to become “super-investors.”

Through our generative AI technology implemented into brokerage platforms and other financial institutions’ infrastructures, we offer instant fundamental analyses of global stocks alongside bespoke investment strategies, enabling informed investment decisions for millions of investors worldwide.

Must have skills:

• At least 5 years of commercial experience in Data Science
• Strong knowledge of linear algebra, calculus, statistics, and probability theory
• Proficiency in algorithms and data structures
• Experience with Machine Learning libraries (NumPy, SciPy, Pandas, Scikit-learn)
• Experience with at least one Deep Learning framework (TensorFlow, Keras, or PyTorch)
• Knowledge of modern Neural Network architectures
• Experience in developing solutions with LLMs
• Experience with Cloud Computing Platforms (AWS, Google Cloud, or Azure)
• Practical experience with Docker
• Experience with SQL
• Strong understanding of Object-Oriented Programming (OOP) principles
• Hands-on experience in building solutions for financial domain
• At least an Upper-Intermediate level of English (spoken and written)

Would be a plus:

• Experience with MLOps solutions
• Basic understanding of Big Data concepts
• Experience in classical Computer Vision algorithms
• Participation in Kaggle competitions

Your tasks will include:

• Full-cycle data science projects
• Data analysis and data preparation
• Development of NLP/Deep Learning / Machine Learning; Developing models and deploying them to production
• Sometimes, this will require the ability to implement methods from scientific papers and apply them to new domains

We offer:

• Delivering high-end software projects that address real-world problems
• Surrounding experts who are ready to move forward professionally
• Professional growth plan and team leader support
• Taking ownership of R&D and socially significant projects
• Participation in worldwide tech conferences and competitions
• Taking part in regular educational activities
• Being a part of a multicultural company with a fun and lighthearted atmosphere
• Working from anywhere with flexible working hours
• Paid vacation and sick leave days

Join Quantum and take a step toward your data-driven future.

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

About us

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

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

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

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

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

What you will do 

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

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

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

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

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

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

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

Qualifications and experience needed 

Education & Experience:

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

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

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

NLP Expertise:

• Deep understanding of natural language processing techniques and algorithms.

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

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

Advanced NLP/ML Techniques:

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

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

ML & Programming Skills:

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

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

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

Data & Analytics:

• Solid understanding of data analytics and statistics.

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

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

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

Deployment & Tools:

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

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

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

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

Leadership & Communication:

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

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

A plus would be 

LLM training & evaluation experience:

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

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

Research & Community:

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

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

Domain & Language Knowledge:

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

MLOps & Infrastructure:

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

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

Problem-Solving:

• Creative mindset for tackling open-ended AI challenges.

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

What we offer

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

• Remote onboarding.

• Performance bonuses.

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

• Health and life insurance.

• Wellbeing program and corporate psychologist.

• Reimbursement of expenses for Kyivstar mobile communication.

We are 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 proficiency in Python
• Extensive experience in DL and PyTorch framework for CV stacks
• Understanding of geometrical computer vision principles
• Model optimization: quantization, pruning, neural network Compiler
• Hands-on experience in implementing low-level CV algorithms
• Knowledge of C++

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

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

• 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

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.