Jobs ML / AI

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest AI communities in Europe.

About the role:
We are looking for a hands-on Sr. Machine Learning / Computer Vision Engineer with strong practical experience in designing, training, evaluating, and improving vision systems for real-world applications. The ideal candidate combines solid theoretical ML/CV fundamentals with an engineering mindset and is comfortable taking ownership of the full pipeline, from data preparation and experimentation to model optimization and deployment.

Responsibilities:
- Design, train, evaluate, and improve machine learning and computer vision models for real-world production use cases
- Build and maintain end-to-end ML pipelines, including data collection support, preprocessing, feature engineering, training, validation, and inference
- Develop solutions across both computer vision and machine learning tasks depending on business needs
- Perform error analysis, diagnose model weaknesses, and drive improvements in accuracy, robustness, and reliability
- Select and justify appropriate methods and model architectures based on dataset characteristics, product constraints, and deployment requirements
- Work with challenging real-world data, including noisy, limited, imbalanced, or weakly labeled datasets
- Collaborate with engineering and product teams to translate business problems into practical ML solutions
- Contribute to production-ready development with a focus on maintainability, reproducibility, and performance

Requirements:
- 3–5+ years of professional experience in Machine Learning / Computer Vision
- Strong senior-level practical expertise in computer vision, machine learning, and applied model development
- Hands-on experience with tasks such as object detection, image classification, anomaly detection, feature-based approaches, or similar
- Solid understanding of classical ML methods, including tree-based models, linear models, feature engineering, model selection, and evaluation
- Strong Python skills and practical experience with PyTorch, OpenCV, and common ML tooling
- Experience building full ML workflows including data preparation, augmentation, preprocessing, training, validation, testing, and deployment handoff
- Strong understanding of model evaluation, debugging, overfitting, generalization, optimization, and experiment-driven improvement
- Ability to write clean, modular, maintainable code and independently own technical solutions
- Strong analytical thinking, engineering maturity, and problem-solving skills

Nice to Have:
- Experience in adjacent ML domains such as time-series, audio, or NLP
- Experience with embedding-based methods, metric learning, retrieval systems, or memory-bank approaches
- Practical experience with model optimization techniques such as quantization, pruning, distillation, or related efficiency-focused approaches
- Experience working with real-world industrial datasets and ambiguous problem formulations
- Knowledge of software engineering best practices, reproducible experimentation, and pipeline design

We offer:
- Free English classes with a native speaker and external courses compensation;
- PE support by professional accountants;
- Medical insurance;
- Team-building events, conferences, meetups, and other activities;
- There are many other benefits you’ll find out at the interview.

Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest 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 seeking an experienced Data Scientist with a passion for large language models (LLMs) and cutting-edge AI research. In this role, you will design and prototype data preparation pipelines, collaborating closely with data engineers to transform your prototypes into scalable production pipelines, design and implement a state-of-the-art evaluation and benchmarking framework to measure and guide model quality, and do end-to-end LLMs training. 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.

Responsibilities:
- Curation of datasets for pre-training, supervised fine-tuning, and alignment
- Research and develop best practices and novel techniques in LLM training and evaluation pipelines.
- Collaborate closely with data engineers, annotators, linguists, and domain experts to scale data processes, define evaluation tasks and collect high-quality feedback.

Requirements:
Education & Experience:
- 3+ years of experience in Data Science or Machine Learning, preferably with a focus on NLP.
- An advanced degree (Master’s or PhD) in Computer Science, Computational Linguistics, Machine Learning, or a related field is highly preferred.
GenAI & NLP Expertise:
- Practical experience with fine-tuning LLMs / VLMs models
- Hands-on experience with modern NLP approaches, including embedding models, semantic search, text classification, sequence tagging (NER), transformers/LLMs, RAGs.
ML & Programming Skills:
- Strong experience with deep learning frameworks such as PyTorch or JAX for building models.
- Ability to write efficient, clean code and debug complex model issues.

Nice-to-have:
Advanced NLP/ML Techniques:
- Applied experience using Reinforcement Learning in NLP / LLM settings
- Prior work on LLM safety, fairness, and bias mitigation.
- Experience generating and curating synthetic datasets for Supervised Fine-Tuning (SFT), including quality control and scaling considerations.
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.
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.
- 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.
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

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

Responsibilities:

• Work closely with different departments (Marketing, Sales, HR, Finance, etc.) to understand their workflows and identify opportunities to improve efficiency using AI tools.
• Analyze existing processes and propose automation or AI-assisted solutions.
• Implement AI-powered automations using available tools (LLMs, workflow automation platforms, data tools, etc.).
• Build and maintain internal AI workflows, prompts, and integrations.
• Train and support team members in using AI tools in their daily work.
• Monitor performance of implemented automations and continuously improve them.
• Document AI workflows, best practices, and internal guidelines.
   

Requirements:

• Strong curiosity about AI and automation and motivation to continuously learn.
• Basic understanding of AI tools (e.g. LLMs, automation platforms, data tools) and how they can be applied in business processes.
• Analytical mindset and ability to break down workflows and identify inefficiencies.
• Basic technical skills (e.g. working with APIs, automation tools, spreadsheets, or scripting is a plus).
• Experience in operations, data analysis, product, or technical roles is a plus.
• Ability to communicate with both technical and non-technical teams.
• Problem-solving mindset and willingness to experiment with new tools.
• Self-driven and proactive approach to identifying improvement opportunities.
   

Nice to Have:

• Experience with automation tools (Zapier, Make, n8n, etc.).
• Experience with prompt engineering and LLM-based workflows.
• Basic knowledge of Python, SQL, or data analysis.
• Experience implementing internal tools or process improvements.

We offer:

• Convenient work schedule (8/9/10/11 — 17/18/19/20);
• 21 working days of vacation;
• 10 working days of sick leave;
• Extra days off for birthday, wedding, etc and for public holidays;
• Payment for training;
• Financial and intangible gifts from the company;
• Free communication, the ability to influence processes and implement your ideas;
• Comfortable and easy management, no tyranny.
   

How We Hire:
We’re not here to make you jump through flaming hoops (unless that’s your hobby — in which case, we’ll cheer you on). Our goal is to see if we’re a good match for each other — and to make sure you enjoy the ride.
1. Pre-Screen Call — 30 min
A quick hello with one of our recruiters. We’ll talk about your background, your goals, and what you’re looking for. In return, we’ll tell you more about ABXplay, the role, the team, and answer any burning questions.
2. Hard Skills Interview — 30–60 min
You’ll meet your potential manager. We’ll dive into the real stuff — your future responsibilities, the challenges ahead, and the kind of work you’d actually be doing (no mystery here).
3. The Offer Stage

Workload: full-time/part-time
Location: Israel, Tel Aviv!
Start: in a couple of weeks
English: B2

Company Description
Our company develops advanced AI solutions for life science, pharmaceutical, and biotech organizations. The company helps clients design practical, high-impact AI systems that improve research, development, knowledge management, and business processes. A key focus is building AI Knowledge Libraries that enable companies to structure, connect, and use their organizational knowledge more effectively with AI:

Role Description
This is a remote contract role for an AI Architect. The AI Architect will be responsible for designing and implementing enterprise AI solutions to specific use-cases, with a strong focus on designing AI Knowledge Libraries for companies in the life science, pharmaceutical, and biotech sectors.

Qualifications
Expertise in Architecture and Architectural Design for scalable and efficient AI systemsStrong experience with Microsoft AzureStrong experience with Microsoft 365 Copilot / Copilot StudioExperience designing and implementing enterprise AI solutionsGreat person to discuss on AI strategy, innovation and practical business opportunities

Domain: SaaS
Project: AI strategy product
Client: Germany
Location: Remote (attending events once a quarter across the EU)
Time Zone: CET
Contract: B2B

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!
Our mission is to connect exciting projects with top-tier professionals. We are committed to creating a dynamic space where innovation thrives, top talent excels, and global teams unite to develop cutting-edge solutions.

About PROJECT
The company 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. The 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, the company’s 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 the product, they 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 their leadership in the AI-driven procurement technology landscape, showcasing our unwavering commitment to innovation and excellence.

Main Tech Stack
AI/DS/ML related stack

Requirements

- Have 5+ years of proven industry experience in applying Machine Learning and AI techniques.
- Demonstrate proficiency in Machine Learning principles and generative models, with an understanding of computational limitations and data privacy considerations.
- Showcase expertise in NLP, LLM (3+ years), 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.
- Demonstrate proficient English communication skills, both verbal and written, with a focus on clarity, openness, and professionalism.

Responsibilities

- 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.

Will be a plus

- Seize the opportunity to join the team and make 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.

Benefits

- A long-term role in a stable, fast-growing company
- Flexible working hours with a results-oriented approach
- Competitive salary and benefits package (vacation, sick leave, national holidays)
- Comfortable workspace, corporate events, and team-building
- Continuous learning, professional growth, and skill development

If you're looking for a place to grow, innovate, and make an impact, we'd love to have you on board!

8+ years software engineering experience

3+ years hands-on ML / AI engineering experience

Strong Python (mandatory)

Proven production experience with LLM systems

Deep understanding of:

- RAG architecture

- Embeddings

- Chunking strategies

- Retrieval optimization

Experience with AWS Bedrock

Experience designing tool-calling LLM systems (MCP or equivalent architecture)

Experience integrating external APIs as agent tools

Experience building REST services (FastAPI or similar)

Experience with vector databases (OpenSearch, Pinecone, etc.)

Experience implementing evaluation frameworks for LLM quality

Experience mitigating hallucinations and prompt instability

Strong understanding of:

- Token economics

- Context window constraints

- Cost-performance tradeoffs

Experience integrating with GitHub and JIRA APIs

Production experience beyond notebooks or PoCs

Job Responsibilities

Architect and implement RAG pipelines on AWS Bedrock

Design knowledge ingestion pipelines (JIRA, GitHub, Confluence, S3)

Define chunking, embedding, and retrieval strategies

Design vector storage and retrieval architecture

Architect tool-calling agent systems (MCP or equivalent)

Define when to use:

- Direct API invocation

- Lambda-based tools

- MCP-based tool integration

- Design and implement structured output agents

Implement hallucination mitigation strategies

Build evaluation pipelines for model quality and regression testing

Optimize token usage and latency

Define model routing strategies (cost vs quality)

Implement guardrails and structured validation

Work closely with DevOps to productionize AI services

Mentor AI engineers and define AI engineering standards

Define long-term GenAI roadmap and architecture patterns

Department/Project Description

We are building an AI-driven SDLC automation platform leveraging AWS Bedrock, knowledge bases, domain agents, and tool-based orchestration.
We are looking for a Lead AI Engineer to design and implement the AI architecture, including RAG pipelines, agent systems, tool-calling mechanisms (MCP-style architectures), model evaluation, and guardrails.
This role requires real production GenAI experience, not experimentation.

Trinetix is looking for a skilled AI-Native Engineer.

We are building an AI-native ecosystem that will power both our internal SDLC and the next generation of AI-driven solutions for our clients. Our R&D organization is developing context-aware AI agents, intelligent engineering tools, and graph-based knowledge systems that integrate directly into real development workflows. 

This initiative will reshape how software is planned, built, tested, and evolved—unlocking entirely new capabilities for our teams and our clients’ product engineering organizations. 

Requirements 

• Hands-on experience building RAG systems, vector search, or semantic pipelines. 
• Familiarity with knowledge graphs (Neo4j, RDF, property graphs, graph DBs). 
• Experience working with LLM APIs (OpenAI, Anthropic, Gemini, Llama, etc.). 
• Understanding of embeddings, text processing, chunking strategies, and retrieval optimization. 
• Experience building developer tooling, internal tools, plugins, or automation. 
• Ability to design and prototype systems independently. 

Nice-to-Have 

• Experience building agentic systems (AutoGen, LangGraph, crewAI, smolagents, function-calling agents, etc.). 
• Experience implementing GraphRAG or similar graph-structured retrieval approaches. 
• Familiarity with AI-powered SDLC tools (copilots, code intelligence, static analysis via LLM). 
• Understanding of software architecture and system design. 
• Experience with CI/CD pipelines, DevOps tooling, or platform engineering. 
• Ability to create ontologies or domain data models. 
• Experience working in R&D would be preferable. 

Core Responsibilities 

• Designing the core models and abstractions that will underpin our AI-native engineering ecosystem. 
• Developing intelligent mechanisms to surface the right information at the right time. 
• Creating assistive capabilities that streamline engineering tasks and workflows. 
• Driving innovation in how software teams interact with knowledge, context, and automation. 

• Collaborating across the organization to embed AI into the end-to-end delivery process. 

   

What We Offer 

• Opportunity to build a first-of-its-kind AI-native SDLC environment 
• Chance to influence engineering workflows across the entire company 
• Work within a high-impact, exploratory R&D team 
• Competitive compensation and growth opportunities 
• Full autonomy to prototype, test, and implement ideas 
• Professional training and English/Spanish language classes   
• Comprehensive medical insurance 
• Mental health support 
• Specialized benefits program with compensation for fitness activities, hobbies, pet care, and more 
• Flexible working hours 
• Inclusive and supportive culture 

Imagine enhancing an AI assistant that already lives on Macs — one that truly understands its users, adapts to their habits, and helps them stay focused on what matters most. Eney is MacPaw’s AI companion for macOS, designed to organize, automate, and simplify users’ daily workflows.

Now, we’re taking Eney to the next level — making it smarter, faster, and capable of running advanced LLMs directly on-device. As a Senior ML Engineer, you’ll fine-tune and optimize the latest LLMs using cutting-edge techniques (LoRA, Q-LoRA, DPO, GRPO), build scalable ML pipelines, and shape how AI seamlessly integrates into everyday Mac experiences.

In this role, you will:

• Train and fine-tune LLMs (e.g., LLaMA, Gemma, Qwen) for specialized tasks (such as function calling, charting, parameter parsing) and ensure their effective deployment across diverse environments, including on-device (MLX)
• Develop and apply model optimization and compression techniques (e.g., dynamic pruning, quantization) to enable high-performance on-device inference
• Conduct rigorous evaluation and benchmarking of LLMs to measure performance, accuracy, and efficiency across different setups
• Adapt LLMs for domain-specific tasks using techniques such as prompt engineering, adapter-based fine-tuning (PEFT), and multi-modal extensions.

Skills you’ll need to bring:

• Strong background in classical ML and NLP
• Solid knowledge of deep learning, going beyond just LLMs (e.g., CNNs, RNNs, Transformers, Autoencoders)
• Proficiency in Python and deep learning frameworks (e.g., PyTorch)
• Deep experience with LLMs: training large and small models using various techniques (fine-tuning, post-fine-tuning, DPO, GRPO)
• Practical experience with LoRA / Q-LoRA and other adapter-based fine-tuning methods
• Knowledge in model optimization (pruning, quantization), evaluation, and deployment
• Ability to design and automate ML pipelines with a focus on scalability and maintainability
• Upper-intermediate English & fluent Ukrainian

As a plus:

• Experience working with multi-modal data: structured/tabular, images, audio, or video
• Experience in synthetic data generation
• Hands-on experience with LLM production deployment: monitoring, evaluation, and metrics tracking
• Familiarity with on-device ML and performance optimization
• Swift knowledge or interest, as the company’s products operate in the Apple ecosystem
• Background in computer vision, time series, or reinforcement learning

What We Offer:

• We are a Ukrainian company, and we stand with Ukraine against the russian aggression
  We maintain workplaces for the mobilized Macpawians and provide financial support to colleagues or their families affected by the war. Here, you can also read about the MacPaw Foundation, which intends to help save the lives of Ukrainian defenders and provide relief to as many civilians as possible.
• We are committed to our veterans
  Our Veteran Career and Empowerment Program is designed to ensure our veterans and active military personnel receive the recognition, support, and opportunities they deserve.
• Hybrid work model
  Whether to work remotely or at the hub is entirely up to you. If you decide to mix it, our Kyiv office, which works as a coworking space, is open around the clock. The office is supplied with UPS and Starlink for an uninterrupted work process.
• Your health always comes first
  We guarantee medical insurance starting on your first working month. For those abroad, you can receive a yearly Medical insurance allowance as compensation for managing your medical expenses.
• Flexible working hours
  You can choose a schedule that is comfortable for you. No one here tracks your clock in/out because MacPaw is built on trust and cooperation.
• Space to grow both professionally and personally
  Education opportunities to grow both hard and soft skills, annual development reviews, and internal community.
• Teams we are proud of
  We build honest, transparent, and reliable relationships within teams. Every Macpawian can improve processes and implement their ideas. We encourage open and constructive feedback and provide training for Macpawians on giving and receiving feedback.
• Office designed for people (and pets)
  Our office has it all: a spacious workplace with enough room for sitting up, lying down, and running around; a gym for recreation; cozy kitchens; a sleeping/meditation room; and a terrace with a view where we throw summer parties. Also, we have two cats living in the office, and you are welcome to bring your pets to the office (we have separate floors for cats and dogs).
• Time-off policy that covers life’s needs
  Convenient personal time-off policy to help you take care of essential matters in your personal life, and parental leaves. On top of all that, sabbaticals are open after 5 years of being with MacPaw.
• Join social initiatives with MacPawCares
  MacPaw participates in numerous humanitarian aid and charity projects across many fields, and you are welcome to jump in to make the world a better place.
• We’re an equal-opportunity employer. Here is a safe place for applicants of all backgrounds
  We are hiring talented humans. Meaning with all our variety of backgrounds and identities, including service members and veterans, women, members of the LGBTQIA+ community, individuals with disabilities, and other often underrepresented groups. MacPaw does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
  
  *Some benefits are under development, and new adjustments are possible.

We present an engaging opportunity to explore and work with various technologies beyond ChatGPT prompts. You’ll have the flexibility to transition between teams, enabling you to contribute to diverse projects, including complex LLM training pipelines, 3D generative AI, diffusion models, and multimodal edge ML.

Our team comprises experts in the foundational elements of next-generation AI systems, encompassing multimodal inference, generative reasoning, and robust training pipelines. We also maintain an in-house GPU cluster, providing resources for interacting, training, and experimenting with larger models for both professional projects and personal exploration.

Joining our team — means joining a knowledgeable group of researchers and engineers in an environment eager to stay at the forefront of AI innovation, a critical endeavor in today’s rapidly advancing AI landscape.

Requirements:
• Strong Python Experience (1 year +)
• Experience with building Generative AI projects
• Specialized experience with LLMs, LangChain, LangGraph, LangFuse, RAG, and related technologies
• Experience in building REST APIs, knowledge of Docker, relational & vector DBs
• At least upper intermediate written/spoken English
• Be able to visit the office at least 3 times a week

Would be a plus:
• Experience with building Voice-to-Voice AI Systems using LiveKit or similar frameworks
• Knowledge of existing yet fresh AI tools, AI marketplaces, etc.
• Knowledge of various machine learning methodologies, including classic machine learning, recommendation engines, natural language processing, computer vision, and beyond
• C/C++ knowledge
• Experience in edge ML

Key Responsibilities:
• Tackle tough problems and find creative ways to solve them
• Maintain and optimize the solution for maximum speed and flexibility
• Guide, tune, and train LLMs
• Write clean and reusable code
• Improve your skills and stay up-to-date with emerging technologies

What We Offer:
• Startup culture, a strong goal-oriented team, and a research mindset
• Opportunity to leverage your engineering skills for fellow engineers and shape the future of AI
• Working with the newest technical equipment (Latest MacBook Pro)
• 20 working days of annual vacation leave
• English courses, Educational Events & Conferences
• Medical insurance

About The Position

Automat-it is where high-growth startups turn when they need to move faster, scale smarter, and make the most of the cloud. As an AWS Premier Partner and Strategic Partner, we deliver hands-on DevOps and FinOps and GenAI support that drives real results.

We work across EMEA and the US, fueling innovation and solving complex challenges daily. Join us to grow your skills, shape bold ideas, and help build the future of tech.

We seek an experienced Senior AI Engineer to join our team. You will work hands-on with innovative startups' Data Science and R&D teams and be in charge of various projects. You will develop robust MLOps pipelines, create cutting-edge Generative AI solutions, implement the latest technologies, and ensure the customers’ success by overseeing the project from inception to completion.

📍 Work location - remote from Ukraine

If you are interested in this opportunity, please submit your CV in English.

Responsibilities

• Developing backend Generative AI solutions and POCs, utilizing RAG pipelines, agent-based systems, and LLM-powered services
• Taking existing ML or customer-provided code and adapting it for training, fine-tuning, and production deployment using PyTorch
• Developing and optimizing fine-tuning workflows, model evaluation, benchmarking, and inference optimization
• Provision of ML AWS resources and infrastructure primarily using managed and cloud-native services
• Developing ML-oriented CI/CD pipelines using GitHub Actions, Bitbucket, or similar tools
• Deploying Machine Learning models in production and ensuring they are observable and monitorable
• Collaborating with Solution Architects and DevOps teams on scalable and cost-efficient AI infrastructure
• Learning new skills and technologies quickly to stay up to date with rapidly evolving GenAI tools
• Using and writing Terraform libraries for infrastructure deployment
• Maintaining infrastructures and environments of all types, from dev to production
• Security monitoring and administration

Requirements

• Excellent customer-facing skills to understand and address client needs effectively
• Ability to design and implement cloud solutions and to build GenAI pipelines on AWS
• Experience with one or more large language model frameworks like OpenAI SDK, Amazon Bedrock, LangChain, and LlamaIndex
• Fluency in Python, a good understanding of Linux, and mandatory hands-on production experience with PyTorch
• Ability to understand tools used by data scientists and experience with building Generative AI applications beyond notebooks or demos
• Good understanding of supervised and unsupervised learning, and deep learning concepts
• Fluent written and verbal communication skills in English
• Ability to work hands-on, independently, and deliver under real project constraints
• Bachelor’s or Master’s degree in AI-related fields - an advantage
• Working knowledge of some Vector Databases (OpenSearch, Qdrant, Weaviate, LanceDB, etc.) - an advantage
• Experience with Docker and Kubernetes - an advantage
• Experience with Azure or GCP (DevOps/MLOps) for ML or GenAI workloads - an advantage
• ML certification (AWS ML Specialty or similar) - an advantage

Benefits

• Professional training and certifications covered by the company (AWS, FinOps, Kubernetes, etc.)
• International work environment
• Referral program – enjoy cooperation with your colleagues and get a bonus 
• Company events and social gatherings (happy hours, team events, knowledge sharing, etc.)
• English classes
• Soft skills training

Country-specific benefits will be discussed during the hiring process.

Automat-it is committed to fostering a workplace that promotes equal opportunities for all and believes that a diverse workforce is crucial to our success. Our recruitment decisions are based on your experience and skills, recognising the value you bring to our team.

We are looking for a Senior Data Scientist with strong technical expertise and a practical mindset to work on complex data-driven challenges. In this role, you will design and implement ML and GenAI solutions, experiment with modern approaches, and transform ambiguous business problems into scalable, production-ready systems.

Requirements
Must-have

• 4+ years of experience in Data Science
• 4+ years of hands-on experience with Python
• Strong understanding of Machine Learning fundamentals, including:
  • regression and classification models
  • feature engineering and model evaluation
• Practical experience with LLMs and GenAI, including:
  • GPT / ChatGPT or similar LLMs
  • LangChain
  • Retrieval-Augmented Generation (RAG)
• Experience with EDA, data preprocessing, and basic dimensionality reduction (e.g. PCA)
• Hands-on experience with tree-based models (e.g. XGBoost)
• Experience building scalable and high-load solutions
• Ability to write clean, maintainable code
• Participation in technical and architectural decisions
  
  Nice to have
• Experience with NLP libraries (SpaCy, Hugging Face)
• Understanding of Explainable AI (XAI) and model interpretability
• Experience with cloud platforms (AWS / GCP / Azure)
• Ability to translate business problems into ML solutions

• Background in R&D, experimentation, or prototyping

  
  Key Responsibilities

• Develop AI agents that utilize LLM, RAG and langchain approach;
• Implement LLM and Diffusion models to boost business productivity;
• Utilize LLM (LLM Vision) to improve object detection, text classification and extraction;
• Create forecasting, recommendation, and classification models;
• Transform business challenges to AI applications;
• Conduct R&D activities: explore new ML/GenAI techniques and assess their practical impact.
• Collaborate with engineering, product, and business teams to deliver impactful data-driven solutions.
  
  We offer:
• Great networking opportunities with international clients, challenging tasks;
• Building interesting projects from scratch using new technologies;
• Personal and professional development opportunities;
• Competitive salary nominated in USD;
• Paid vacation and sick leaves;
• Flexible work schedule;
• Friendly working environment with minimal hierarchy;

• Team building activities, corporate events.

   

This is not a coding role. This is a systems-of-systems role. 

If you build things with AI, this posting isn't for you. 

If you build the systems that govern how AI builds things — keep reading.

What This Role Is

LifeChef is a HIPAA-compliant healthcare platform delivering Medically Tailored Meals. Our engineering team builds on a methodology called Enso — a spec-driven, AI-augmented development framework where every feature starts with a specification, gets built with AI assistance, and is validated against defined criteria.

Enso v1 is linear: spec → generate → validate → ship. It works. But it's hitting scaling limits — the document graph grows with every feature, validation complexity compounds, and the process requires increasing human oversight to maintain quality. This is a familiar problem in any AI-assisted workflow that outgrows its initial architecture.

We need someone to take Enso from v1 to v2: a true agentic system where specialized AI agents handle research, specification validation, code generation oversight, quality assurance, and deviation detection — with structured autonomy, clear escalation paths, and human-in-the-loop at defined decision points, not everywhere.

You would partner directly with the CTO (who designed the current methodology and has strong opinions about where it needs to go) to architect, build, and maintain this system. The first 30 days are heavy collaboration — translating vision into working architecture. After that, you own the evolution of the process while the CTO focuses on product and team.

What You'd Actually Build and Maintain

• Agent topology for the development lifecycle: research agents, spec validation agents, code review agents, compliance checking agents, red-team/adversarial agents — each with defined boundaries, confidence thresholds, and escalation triggers
• Spec-to-code reconciliation system: agents that validate whether implemented code actually satisfies the specification, flagging deviations and ambiguities rather than silently passing
• Multi-model orchestration strategy: routing different tasks to different models based on cost, capability, and risk (expensive models for reasoning, cheap models for classification, open-source where it fits)
• Structured output validation: ensuring agent outputs are parseable, auditable, and actionable — not just "LLM said it's fine"
• Governance and escalation framework: what agents decide autonomously vs. what requires human sign-off, circuit breakers for low-confidence situations, audit logging for compliance
• Evolution engine: the system should improve itself — tracking where agents fail, where human overrides happen, and feeding that back into prompt refinement and architecture changes
• Integration with existing CI/CD: the agentic layer hooks into GitHub Actions, automated testing (Vitest), PHI scanning, secret detection, and compliance validation already in the pipeline

This is infrastructure that the development team operates within. You're not shipping product features — you're shipping the machine that makes product development faster, safer, and more consistent.

What We Need From You

Hard requirements:

• You have designed or built at least one agentic system where AI agents operate with structured autonomy — multi-step workflows with validation, error handling, and escalation. Not just API wrappers. Not just chatbots.
• You understand agent topology as a design problem: which agents need to exist, what their boundaries are, how they communicate, how you prevent cascading failures.
• You have hands-on experience with LLM orchestration frameworks (LangChain, CrewAI, AutoGen, custom implementations — the specific tool matters less than the depth of understanding).
• You think in terms of eval frameworks and validation: how do you know if an agent's output is good? How do you measure drift? How do you build confidence scoring that's actually useful?
• You understand multi-model strategies: when to use which model, cost/capability tradeoffs, how to tier your model usage so you're not burning expensive tokens on classification tasks.
• You can work in both Python and TypeScript, or at minimum are comfortable in one and willing to operate in the other. Our product stack is TypeScript-first but agentic tooling often lives in Python.
• Strong written communication. This role involves heavy async documentation of architectural decisions, process design, and system behavior.

What sets you apart:

• Experience with Model Context Protocol (MCP) — building custom MCP servers, tool-use architectures
• Claude Code, Cursor, or similar AI-native development tools as part of your own workflow (you should be using what you're building systems around)
• Experience in healthcare, fintech, or regulated environments where AI outputs must be auditable and compliant
• Understanding of prompt engineering at the systems level — not "write a good prompt" but "design a prompt architecture that's maintainable, versionable, and testable"
• You've thought seriously about adversarial validation — red-team agents, cross-model verification, structured disagreement as a quality signal
• You have opinions about where agentic AI is overhyped and where it genuinely works, and you can defend those opinions with specifics

What we explicitly don't require:

• A portfolio of 50 agentic projects. The field is young. One deep project with real orchestration depth beats ten shallow ones.
• Product development skills. You won't be building UI or shipping user-facing features.
• Healthcare domain expertise. We'll teach you the domain. You teach us the orchestration.

How We Evaluate

1. Screening questions (in your application) — We're looking for specificity and honesty, not keyword density.
2. Architecture conversation with CTO (90 min) — We'll describe our current system, its limitations, and where we want to go. You'll tell us how you'd approach it. This is a two-way design conversation, not a quiz.
3. Small design exercise (async, take-home) — Given a simplified version of our spec validation problem, design an agent topology. We're evaluating how you think about boundaries, failure modes, and escalation — not production-ready code.
4. Trial sprint (60–90 days) — Heavy pairing with CTO in the first 30 days, increasing autonomy after. Defined milestones at 30, 60, and 90 days.

The Team and Context

Small, senior engineering team, primarily Ukraine-based. The CTO (Jay) designed the Enso methodology, has 20+ years of engineering experience across healthcare, threat intelligence, and enterprise platforms, and has built the governance architecture (constitutions, spec-gates, validation layers) that you'd be evolving. He's technical, direct, and has strong opinions — but he's hiring you because he needs a partner in this, not an executor.

The product stack is Next.js 16, React 19, TypeScript, PostgreSQL, AWS serverless (Lambda, CDK, Amplify Gen 2), with Vitest testing, Pino structured logging, and HIPAA compliance enforced in CI. You don't need to know this stack deeply, but you need to understand it well enough to build agentic tooling that integrates with it.

We're building toward autonomous agentic systems for clinical operations — this development process work is the foundation that makes that possible. If the idea of designing the system that governs how an engineering team builds software excites you, this is the role.

How to Apply

Reply with:

• The most complex agentic or LLM orchestration system you've built — what it did, how it was structured, what you learned
• Your honest take on where agentic AI is right now and what's actually hard
• What interests you about building process infrastructure vs. product features

Generic responses get skipped. We know the field is young — we'd rather hear about one deep experience than a list of tools you've touched.

Description

Our Client is the creative transformation company. We use the power of creativity to build better futures for our people, planet, clients and communities. 

In a world where media is everywhere and in everything, we bring the best platform, people, and partners together to create limitless opportunities for growth.

Global data products and technology company. We’re on a mission to transform marketing by building the fastest, most connected data platform that bridges marketing strategy to scaled activation.

We work with agencies and clients to transform the value of data by bringing together technology, data and analytics capabilities. We deliver this through the Open Media Studio, an AI-enabled media and data platform for the next era of advertising.

We’re endlessly curious. Our team of thinkers, builders, creators and problem solvers are over 1,000 strong, across 20 markets around the world.

Requirements

Essential

• GCP (esp Vertex, BigQuery, Model Registry)
• Python (needs to be very strong)
• Docker
• FastAPI (or similar)
• SQL
• Terraform
• Strong understanding or even hands-on ML experience
• Package management (uv is cool, but don’t mind others e.g. poetry; we work with a whole range here)
• Scalable experimentation & model tracking (no specific tech as we’re using native GCP logging and metadata store atm, but as we mature open-source tech e.g. MLFlow will be brilliant), and Scalable ML deployment experience (i.e. standing up an inference endpoint for hardly no traffic doesn’t count)

Desirable:

• FTI (Feature/Training/Inference) framework
• Common ML frameworks (such as PyTorch, Sklearn)

Bonus:

• RAG
• LLM orchestration tool e.g LangGraph,
• Reinforcement Learning tools e.g. OpenaiGym, RLib

Job responsibilities

The Machine Learning Engineer is responsible for deploying and maintaining the algorithms developed by data scientists. This role will be part of the Optimize Data Science team (3 FTEs plus seconded team of 5 FTEs)

While Data Scientists focus on research and model development, the ML Engineer is responsible for the technical infrastructure, scaling, performance optimization, and maintenance of the models. Their work involves implementing, testing, deploying, and monitoring the models in a production environment.

• Model deployment: Takes models (brand new or improvements to existing models) developed by data scientists and builds the software and infrastructure to deploy them into a live production environment.
• Performance optimization: Optimizes code for latency and efficiency across different hardware, like CPUs and GPUs, and overall quality (e.g. readability, maintainability, reliability and so forth).
• System design & scaling: Designs, builds, and maintains the technical components that integrate into existing software to train, deploy, and scale ML models. (N.B. There will be opportunity to work on new systems from ground up later in the year, too.)
• Monitoring and maintenance: Implements logging and monitoring to track model performance, identifies and fixes bugs (in collaboration with wider teams if appropriate), and performs necessary updates and improvements.
• Collaboration: Works closely with data scientists and wider engineering teams to understand the model and help convert it into a production-ready system.
• Experimentation: Design, build and deploy technical components based on the methodologies designed by Data Scientists to enable scalable experiments, model evaluation and visualisation of results.
• Up-skilling DS in ML Engineering and AI innovations: Support DS team to utilise modern and cloud-based (esp GCP) technologies for development (e.g. Vertex, Docker, BigQuery, Dev Containers, Ray and others) to expedite and innovate the entire development lifecycle –esp when moving from dev into prod

About the role

We are building an AI assistant for the legal domain that will combine:

• Voice AI agents that can call users and collect structured information
• LLM-based workflows for conversation and reasoning
• RAG system for answering questions based on legal documents
• Knowledge base for structured legal information

The system will work with voice calls, documents, and conversational interfaces, helping automate legal workflows and client communication.

Responsibilities

• Design architecture for AI agents and LLM workflows
• Build voice interaction pipelines
• Develop RAG pipelines for document retrieval
• Integrate LLMs with APIs and internal systems
• Work with vector databases and embeddings
• Optimize prompts, latency, and reliability of AI responses

Tech stack (expected)

Strong experience with:

• Python
• LLM APIs (OpenAI / Anthropic)
• RAG systems
• Vector databases

Nice to have:

• LangChain / LangGraph / LlamaIndex
• Twilio / VAPI / Retell
• Speech-to-Text / Text-to-Speech

• FastAPI

   

Ideal candidate

Someone who has already built:

• AI agents
• RAG systems
• conversational AI
• integrations between LLMs and real products

We are looking for an AI/ML Engineer to help build AI-powered systems using modern LLM architectures.

Responsibilities:

• Design the pipeline from input processing to generation and streaming.
• Implement file processing and context preparation.
• Build semantic search and summarization using embeddings and vector databases.
• Develop structured and hierarchical LLM outputs.
• Design agent architectures (roles, context management, JSON Patch updates).
• Select and implement frameworks such as LangGraph, LangChain, or alternatives.

• Optimize performance, reliability, and error handling.

   

Requirements:

• Strong Python and AI/ML experience.
• Experience with LLMs, RAG pipelines, embeddings, and vector DBs.
• Familiarity with AI orchestration frameworks (LangChain, LangGraph, etc.).
• Understanding of agent-based systems and structured outputs.

• Good English communication.

   

Format: Part-time collaboration, flexible schedule.

What We Offer :

 Professional Growth & Support:

• Mentorship: Hands-on learning from senior experts.
• Career Growth: A clear and documented path to advance.
• Feedback: Continuous support and regular performance reviews.

• Accounting: Full paperwork and tax support managed by us.

   

 Work-Life Balance & Flexibility

• Flexibility: Work from home or our Lviv office (Hybrid).
• Vacation: 18 days of paid annual leave.
• Extra Leave: 10 additional paid days off every year.

• Sick Days: 5 paid days for your health and recovery.

   

 Culture & Office Life

• Office Perks: Free lunches for those working from the office.
• Team Events: Regular gatherings and community-building activities.
• Celebrations: Personal gifts for your work milestones.
• Team Spirit: Corporate gifts and a supportive atmosphere