Jobs ML / AI

We are looking for a Data Solution Architect to make our team even stronger.

Kindly note that this role supports remote work, but only from within Ukraine.

Responsibilities

• Work closely with business in identifying solution requirements and key case-studies/scenarios to architect Data solution
• Participate in Data and Big Data initiatives on a company level
• Design Data analytics solutions by utilizing the Data technology stack with Big Data techniques
• Conduct solution architecture review/audit, calculate and present ROI
• Create and present solution architecture documents with deep technical details to customer and implementation teams
• Participate in specific “pre-sale” activities related to available experience and practice in order to prepare technical proposals on customer requests
• Lead implementation of the solutions from establishing project requirements and goals to solution “go-live”
• Maintain a strong understanding of technical solutions and architecture design trends and best practices, stay on cutting edge of Data technologies
• Constantly grow expertise by gathering and monitoring available EPAM project experience and ongoing projects with clients from different business domains to drive further EPAM business in Data field
• Create and follow personal education plan in the technology stack and solution architecture
• Share experience, knowledge and vision with EPAM, colleagues and customer teams. Participate in seminars, meet ups, mentoring, training programs. Prepare white papers and presentations

Requirements

• Strong ‘hands-on’ experience as a Data Architect with design/development background with Java, Scala, or Python
• Architecture experience and practice in Data Management, Data Storage, Data Visualization, Disaster Recovery, Integration, Operation and Security
• Experience with building traditional Cloud Data Warehouses, Data Lakes
• Knowledge of high load and IoT Data Platform architectures and infrastructures
• Wide experience in analysis, design, implementation, deployment as well as troubleshooting and rebuilding distributed Linux based platforms and Big Data solutions on premises and in Cloud
• Strong Cloud experience in at least one of the Cloud providers (AWS, Azure, GCP)
• Solid experience in continuous delivery tools and technologies
• Broad experience with Containers and Resource Management systems: Docker, Mesos, Kubernetes/OpenShift, Yarn
• Able to deliver Data Analytics projects and architecture guidelines
• Strong in research, comparison, and selection of tools / technologies / approaches to be used
• Practical experience in performance tuning and optimization, bottleneck problem analysis
• Strong communication skills, experienced in team coordination skills and solution implementation supervision
• Good in Agile development methodology, Scrum in particular
• Solid skills in business analysis, network/stack architecture, troubleshooting, support
• Fluent English (B2 level)

We offer/Benefits

With us you can:

• Work on a flexible schedule remotely or from any of our comfortable offices or coworking spaces in Ukraine
• Receive the necessary equipment to perform your work tasks
• Change projects and technology stacks within EPAM
• Gain experience in various business domains (Insurance, E-commerce, Healthcare, Finance, Travelling, Media, Artificial Intelligence, and more)
• Relocation opportunities may be available for eligible candidates, depending on the role and openings at other EPAM locations
• Participate in volunteer, charity programs and communities (both technical and interest-based)

We focus on your professional growth:

• You can plan your individual career path together with your manager
• Receive regular feedback from colleagues
• Improve your English for free with certified teachers (Speaking Clubs, client interview preparation courses, etc.)
• Get the opportunity to undergo free training and certification in AWS, GCP, or Azure Clouds
• Use the internal E-learn training program (18,200+ specialized training and mentoring programs)
• Access corporate accounts on LinkedIn Learning, Get Abstract and other partner resources
• Study at EPAM Solution Architecture School with the instructors who are practicing architects
• Develop as a leader, join Delivery Management, Resource Management, Leadership Essentials school and more
• Participate in internal communities (500+ meetups, technical discussions, brainstorming sessions, online events and conferences annually)

What we offer:

• Vacation and sick leave (including a sick leave without a medical certificate)
• A wide range of Voluntary Medical Insurance programs providing both medical treatment and various preventive options (including sports activities)
• Medical insurance for family members at corporate rates
• Company support during significant life events (childbirth or adoption, marriage, etc.)
• Support for psychological comfort: discounts on services from mental health specialists or coaches, thematic training
• E-kids program — a free programming language training program for EPAMers’ children

Kindly be advised that the set of benefits, including learning, certification, and other opportunities, may vary depending on the role you apply for. Our recruiter will be able to share more details about the specific opportunity during your general interview.

ABOUT EPAM

EPAM strives to provide its global team of over 62,350 professionals in more than 55 countries with opportunities for professional growth from day one of collaboration. Our colleagues are the source of EPAM’s success, so we value cooperation, strive to always understand our clients’ business and aim for the highest quality standards. No matter where you are, you will join a dedicated, diverse community that will help you realize your potential to the fullest.

We are looking for a Computer Vision Engineer to join our team and develop vision-based systems for UAV applications.
In this role, you will work on the development of computer vision algorithms and their deployment on resource-constrained devices. You will be involved across the development cycle, from algorithm development and hardware evaluation to testing, performance optimization, and deployment.
This is a product-facing role where you will work closely with stakeholders from multiple departments and teams to develop reliable vision-based solutions for real-world applications.
​
Responsibilities:

• Develop and optimize computer vision algorithms, focusing on tasks such as object detection, tracking, and navigation for real-time edge inference
• Implement and customize machine learning models and vision frameworks, ensuring seamless integration with existing drone systems and hardware
• Conduct extensive testing and validation of algorithms in real-world scenarios, fine-tuning parameters and optimizing performance for robust performance in various conditions
• Evaluate and provide input on appropriate camera sensors, lenses, and onboard computing hardware for vision applications
• Develop repeatable testing methods and build data pipelines to evaluate and continuously improve the performance of vision models

• Collaborate with computer vision and robotics experts to leverage the latest advancements in the field and integrate them into the systems

   

Requirements:

• Proven ability to develop high-performance, real-time applications in C++ or Rust, specifically leveraging ROS2 for distributed messaging and OpenCV for real-time image processing or feature detection
• Strong hands-on experience developing and implementing computer vision systems for real-world, real-time embedded applications
• Deep understanding of both traditional computer vision techniques (e.g., feature extraction, optical flow, camera calibration) and modern deep learning architectures (e.g., CNNs, Vision Transformers)
• Track record of having implemented and deployed vision applications on robotic platforms in real applications
• Strong analytical and problem-solving skills, with the ability to design and evaluate complex algorithms in real-world scenarios
• Effective communication skills and the ability to work collaboratively in multidisciplinary teams, interacting with researchers, engineers, and external stakeholders
• Ability to effectively navigate ambiguity and lead across in a fast-paced environment

• English at B2–C1 level for communication with international colleagues and working with technical documentation.

   

We Offer:

• Official employment under Ukrainian labor law
• Military booking
• Medical insurance
• Individual English lessons
• Stock Options
• Opportunity to work on cutting-edge UAV systems and grow professionally

Are you a Data Scientist with a love of LLMs, generative AI?

We are looking for a passionate Data Scientist to implement AI solutions aimed at achieving business goals.

This role offers the opportunity to work on cutting-edge AI adoption projects that helps to improve current business processes.
 

     You'll be a great fit if you have:

•  Strong Python Experience (2 year +);
• Experience with LLM , Diffusion models;
• Knowledge of Prompt engineering;
• Experience with Gen AI-related technologies such as LangChain and RAG;
• Experience with Neural Networks (Optional) ;
• Experience with NLP , Predictive analytics and Machine learning;
• Experience with Pandas;
• Experience with SQL, including experience with large datasets;
• Strong experience in statistics;

• Bachelor's degree in Computer Science or a related field.
   

  What you'll do:

• 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.
   

     We ensure your growth with:

• Competitive salary fixed in USD;
• Flexible working schedule and fully remote work format;
• Paid vacation days and sick leave days ;
• Personal and professional development opportunities;
• Participation in building innovative projects from scratch using modern technologies;
• Team-building activities and corporate events;
• English classes and educational events.

N-iX is seeking a motivated Trainee AI Engineer to join our dynamic team and working on the AI solution for the global biopharmaceutical company. This role offers an excellent opportunity to work with cutting-edge AI technologies to help create innovative solutions that provide engaging and insightful experiences for users. The ideal candidate will have a basic understanding of machine learning and a keen interest in developing AI applications. While our project uses Palantir Foundry, prior experience with it is a plus, but not mandatory.

Requirements:

• Currently pursuing or recently completed a Bachelor's degree in Computer Science, Artificial Intelligence, or a related field.
• Basic programming skills in Python.
• Familiarity with machine learning concepts and frameworks like TensorFlow or PyTorch.
• Interest in natural language processing and computer vision.
• Willingness to learn and work with cloud services (AWS, Azure, or GCP).
• Ability to work collaboratively in a team environment.
• Strong problem-solving skills.
• Upper Intermediate+ knowledge of the English language.
• Ukrainian language - advanced or higher.

Responsibilities:

• Assist in developing AI solutions, including chatbots and computer vision applications.
• Support the integration of AI solutions into existing platforms and workflows.
• Help optimize machine learning models for performance, accuracy, and user engagement.
• Contribute to building and optimizing retrieval-augmented generation (RAG) systems and vectorized databases.
• Monitor and evaluate the performance of AI applications, identifying areas for improvement.
• Stay informed about developments in AI, machine learning, and related fields to contribute to innovative solutions.
• Assist in developing and maintaining documentation related to AI models and applications.
• Work with data engineers to support data preparation for training and fine-tuning models.
• Support the implementation of security measures to protect sensitive information processed by AI applications.

Nice to have:

• Experience with AI projects through coursework or personal projects.
• Understanding of RESTful APIs and how applications integrate.
• Familiarity with version control tools like Git.

We are currently looking for a Senior Voice AI Engineer, Evaluation and Reliability to join a professional German-based product company team that designs innovative eHealth solutions for hospitals.

You will work on a stable healthcare product.

Senior level, and we mean it. You would own the evidence that this system is safe to put in front of

patients, on your own judgement, and tell us when the answer is no. That isn't a job we can hand to

someone still learning what to measure.

You would not be building a voice stack. We use a third-party voice platform that already handles the

telephony, the speech recognition and synthesis, and the knobs for end-of-speech detection and

interruption. What it does not do is tell us whether any of that is working well enough for patients, and it

gives us no way to check that a change hasn't made things worse. That's the job.

So you'd own the conversation and the evidence about it: how the assistant behaves when a caller

interrupts, corrects themselves or won't answer; how fast the whole thing responds once a round trip to

our backend is in the loop; and the body of test cases we run before every change.

You need

• To have run a phone or voice assistant that took real calls from real users, and to be able to tell us what its response time was and how you measured it. Having built the audio pipeline yourself is fine but not the point — we care that you know what to measure and what good looks like.
• To have tuned a voice system rather than only shipped one: end-of-speech detection, barge-in, how long to wait before assuming the caller has finished. You'd be doing this against a vendor's settings rather than your own code, and their own documentation says clean barge-in can't be guaranteed on phone audio.
• To know what happens to speech recognition accuracy when the audio comes down a phone line instead of a good microphone, and to have measured it rather than read about it.
• A way of testing conversation changes that doesn't rely on listening to a few calls and hoping. This is the centre of the role. Whatever form it took — a replay harness, a scored scenario set, anything — we want to hear how it worked and where it let you down.
• Enough Python to build that harness and the tooling around it.
• We are considering candidates who work through Ukrainian FOP.

Nice to have

• Having worked against a closed voice platform, with the frustrations that brings, rather than only your own stack.
• Telephony background: SIP , WebRTC, Asterisk, Twilio or similar. Useful for knowing why calls sound the way they do, though you would not be running any of it.
• Work with health data, or any other field where getting it wrong has consequences.
• German, though we're not counting on it.

You don't need

• To speak German.
• To have built streaming audio infrastructure, a turn-taking system or a speech pipeline. The platform does that, and rebuilding it isn't on the table.
• To know PHP or anything about our existing backend. Another team builds the endpoints you'd call.
• To have trained a model from scratch.

We are currently looking for a Senior AI Platform and Model Operations Engineer to join a professional German-based product company team that designs innovative eHealth solutions for hospitals.

You will work on a stable healthcare product.

You'd own the models and the platform around them, serving every channel we put in front of a patient.

The phone assistant is first because it's already underway, but it isn't the point of the job. Next is a chat assistant in the patient portal that can answer questions and walk someone through booking an

appointment, and after that whatever channel comes along.

The thing you're actually building is one internal API that every channel calls, so that the rules about what a patient can do live in one place instead of being written three times, slightly differently. The phone assistant and the web chat are two front ends onto the same thing.

Alongside that: moving off the vendor. Right now we pay per minute for speech recognition and language models, and within about a year we want to be running them ourselves — because we're dealing with

patient data, and because we don't want a vendor swapping a model underneath us without telling us.

To be clear about scope: we mean serving open-weight models and adapting them to our domain. We don't mean training a model from scratch.

One thing we want to be direct about. We are not looking for someone to build what we've already decided on. We don't yet know whether running these models ourselves is the right call — we don't have an accuracy figure on real German phone audio, and we don't have a proper cost comparison. Your first job is to produce both, and if they say we should keep paying the vendor for another year, we need you to tell us that plainly. Someone who quietly builds the thing because we asked for it is worse than no hire at all. If being the person who says "the numbers don't support this" makes you uncomfortable, this isn't the job.

You need

• To have put a language model behind an API that more than one product called, and kept it running.
• Streaming responses, timeouts, retries, and errors that tell the caller something useful.
• To have run a speech recognition or language model in production on hardware you were responsible for. Not a notebook, not someone else's API.
• To be able to size and cost it: how many people one GPU will serve at once, what an hour of audio or a million tokens costs you, where it starts to fall over.
• Tool calling in production, and a clear view on where the boundary sits. The assistant will guide patients through booking, which means the model proposes an action and our code decides whether it's allowed. If you've built something where the model was effectively trusted to act, we'd want to talk about what you'd do differently.
• To have dealt with untrusted text reaching a model that can trigger actions. A chat box on a patient portal is a much easier thing to attack than a phone call, and someone will try.
• Experience grounding a model on your own content so it stops inventing things — and honesty about where that still fails.
• Experience fine-tuning or adapting an open-weight model, LoRA or PEFT or something equivalent, and the honesty to say whether it was worth the effort.
• A habit of being suspicious of your own benchmark numbers, especially the good ones.
• To understand that using patient conversations to train a model is a different legal question from using them to answer one. If that distinction is new to you, this role probably isn't the right fit.
• Python and Linux, containers, and a serving stack like vLLM or TGI.
• Enough experience to work without an ML team around you, and to tell a team lead something he'd rather not hear.
• We are considering candidates who work through Ukrainian FOP.

Nice to have

• Quantization, and a view on what it costs you in accuracy.
• Audio processing: sample rates, codecs, what a phone network does to a recording.
• GPU scheduling, Kubernetes.
• Having built something where the same assistant had to behave consistently across more than one channel.
• Having worked somewhere GDPR was taken seriously rather than mentioned occasionally.

You don't need

• To have built a voice assistant, or to know anything about telephony. That's the other role, and the phone channel is only one consumer of what you'd build.
• To speak German.
• To know PHP . You'd define what our backend needs to expose; another team builds it.

AI-First Senior Software Engineer

Ukraine, Kyiv · On-Site · Full-Time

Company: Orbox Group - new AI-native product for the oil & petroleum business in Ukraine

Location: Ukraine, Kyiv - on-site, office in the city centre

Employment: Full-time, permanent

Seniority: Senior

Reports to: Tech Lead

Works with: Business Analyst, UI/UX Designer, QA Engineer, business owners & operations team

Languages: English - Advanced (B2-C1); Ukrainian - fluent

The role

This is the role that turns a real offline business into production software - by leading AI agents, not by typing every line yourself.

This is a hands-on senior engineering role. Most of our code is written by AI coding agents against machine-readable specs. That makes senior engineering judgment more valuable, not less: agents write fast, but still pick wrong abstractions, miss edge cases, get money and inventory maths wrong and ship code that passes its own tests and breaks in production. You are the engineer who designs the system, sets the guardrails and owns what gets merged.

We operate in a fast-moving startup environment where speed and precision both matter. You must be opinionated about which engineering tasks AI does well today (boilerplate, refactors, tests, migrations, docs), which it does not (architecture, security boundaries, trade-off calls, debugging subtle production issues) - and how to combine both deliberately.

What you will own & do

AI-First Engineering

• Daily, deep, native use of AI coding agents for implementation from specs, refactoring, test writing, code review, debugging and documentation.

• Treat agents like a team you lead: clear specs in, reviewed code out - you own the context, rules files, guardrails and the review bar.

 Set up the agentic delivery pipeline for the new product from day one: repo conventions, agent instructions, MCP tooling and CI checks that keep agent output safe to merge.

• Contribute to internal evals from the engineering side: define what good” looks like for AI-generated code and for the product's user-facing AI features.

Product Engineering

• Own the architecture from zero: service boundaries, domain and data model, APIs, environments - built for a real operational business with multiple sites and roles.

• Own features end to end: from spec & acceptance criteria to design, implementation, tests, deployment and monitoring on prod.

• Build operational surfaces: dashboards, inventory and stock movements, orders, pricing, documents, approvals, reporting and back-office tooling - including mobile-friendly flows for people in the field.

Integrations & Data

• Integrate with the existing world: accounting and ERP systems, Excel and legacy data, banks and payment providers, fiscalization and regulatory reporting.

• Connect the physical side of the business where relevant.

• Plan and run data migration from spreadsheets and legacy tools - reliably, idempotently, with proper error handling and audit trails.

AI & LLM Systems

• Build production AI features: LLM agents, tool calling, RAG, document processing (waybills, invoices, contracts), reconciliation, forecasting and anomaly detection - with grounding, fallbacks and cost control.

• Engineer the non-deterministic layer: prompt versioning, structured outputs, guardrails against hallucinations and prompt injection, role-safe access to data.

• Instrument AI behaviour with tracing, logging and metrics - treat AI output as something to be measured, not trusted.

Engineering Quality & Ownership

 Own the merge bar: review agent and human PRs, enforce architecture, security and performance standards.

 Write automated tests and CI that make agent output safe to ship; partner with QA on test strategy and release readiness.

• Feed spec & story defects back upstream: when the work item is wrong or ambiguous, it gets fixed at the spec layer with the Business Analyst, not hacked around in code.

• Shape the engineering practice for the product: conventions, environments, observability, incident handling - lightweight enough for startup speed, rigorous enough to trust.

What we are looking for

Must have

 4+ years of hands-on software engineering on production web products, with senior-level ownership of a system or major product area.

 Strong full-stack skills: modern backend (TypeScript / Node.js or Python), modern frontend (React / Next.js or similar) and relational databases (PostgreSQL or similar).

• Daily, deep, native use of AI coding tools (Claude Code, Cursor etc.) in your engineering workflow. You have a real, opinionated workflow built around them and can defend your choices.

• Hands-on experience building LLM-based features: agents, tool calling, RAG, structured outputs - shipped to production, not just demos.

• Solid system design for operational B2B software: transactional data, inventory and money flows, role-based access, API design, async processing, third-party integrations.

• Critical code review skills: you read AI-generated code with suspicion and catch what agents miss - security holes, data leaks, wrong abstractions, hidden complexity.

• Cloud & DevOps fluency: Docker, CI/CD, at least one major cloud (AWS / GCP / Azure), logging and monitoring.

• 0 → 1 mindset: comfortable starting a product from an empty repo, working directly with founders, business owners and product, shipping fast without losing rigor.

• Strong written and verbal communication in English and Ukrainian - including specs, PR descriptions, technical discussions and talking to the people who run the business.

Strong advantage (Nice to have)

• Experience integrating with BAS / 1C, Ukrainian banks and payment providers, fiscalization or tax / excise reporting.

• Document processing (OCR, extraction), forecasting or anomaly detection in production.

• Experience with LLM evals and observability (Langfuse, LangSmith or similar) and vector databases.

Why this role

You will own a new AI-native/AI-first engineering from an empty repo and prove that an AI-first delivery machine can transform a traditional industry, not just a startup niche.

• Genuine engineering ownership - you design the system from zero and hold the merge bar.

 A real business as your first customer - live operations, real users, real volumes and money.

• Clear growth trajectory - Tech Lead / Engineering Lead for this product line.

• Direct access to founders and Head of Delivery & Product.

• AI-first by default.

• Great office in the centre of Kyiv.

The automotive industry moves trillions a year and still runs on a phone queue, a web form and a salesperson who calls back tomorrow. A car is sold, financed, serviced and traded the way it was in 1914, with a website on top. We have set out to put that industry on autonomous rails: commercial operations that run themselves. AI employees that talk to every customer in seconds, in their language, on every channel, that decide, act, book straight into the systems the industry runs on, and get better every day. Dealer groups first. Then the importer, the OEM, the fleet, the finance company, every place a vehicle meets a customer.

It already runs: about a thousand real customer conversations a day for BMW, MINI, Ford and Hyundai dealer groups, and a pilot with a Mercedes-Benz group in Germany. What powers it is not a chatbot. It is a distributed system with language models as components inside it: state, queues, retries, dealer systems that answer in fifteen seconds or not at all, a hundred ways for an output to pass every check and still be wrong. For the next stage we are looking for a strong team member at co-founder level who wants to go deep on autonomous systems, model development, system design, and the real, live deployment of a new breed of system, and who takes the hardest problems because they are the hardest. Flat hierarchy: you are treated as an equal member of the team and your reward is linked to the outcome. The team pushes the frontier relentlessly. There are no meetings and no politics. There is a system in production, customers on the other end, and the next thing to ship.

What you will build and do

1. Own the agent runtime end to end: orchestration, tool execution, memory, handover to humans, integrations into dealer management systems and messaging channels, in production for real customers from your first month.
2. Build evals and traces before features. Every change to an agent ships with a way to prove it did not make things worse.
3. Decide the architecture with the founders: what runs where, which model does which job, what a session may cost, what happens when a component fails.
4. Ship every week. The product is in production; the customers are dealer groups whose customers are waiting on the other end.

Who we look for

1. Has built AI systems from the ground up, not wrappers over an API. Strong senior experience shipping systems in production. A PhD is a plus, not a requirement.
2. Sees an agentic system as a distributed system with the LLM as one component or orchestrator inside it. Thinks in state, failure modes, retries and observability, not just prompts.
3. Uses coding agents effectively as a daily working tool.
4. Builds evals and traces before building features. Understands that a large share of failures here are semantic, not mechanical: output can pass every check and still be wrong.
5. Conscious of cost and latency: how many tokens, how many dollars per session, where a smaller model does the job.
6. Comfortable with incomplete specs. Makes decisions with missing information, documents them, and keeps moving. This is an early stage startup; nobody will tell you what to do step by step.
7. Wants a flat hierarchy and means it: takes on the hardest problems, argues for what is right, and carries the outcome as an equal member of the team, with a reward linked to that outcome.
8. Wants to go deep: autonomous systems, model development and fine tuning, system design, and deployments that real customers depend on the same day. No meetings, no politics, shipping.

What we offer

Role

Full time, remote from wherever you build best, with time together in person when it matters.

Pay

A fixed salary and a share in the company that links you to the outcome, discussed openly in the first conversation.

Work

Direct work with the founder and our AI lead, who built the current system. No meetings, no politics. A product in production with the most complete automotive AI reference in the region; your code answers real customers the week it ships.

How we work

01 Flat hierarchy

You are an equal member of the team. The best argument wins, whoever makes it.

02 Rewarded on the outcome

A share in the company links you to what we build, not to a title.

03 No meetings, no politics

Written decisions, a system in production, customers on the other end.

04 Ship every week

Your work answers real customers the week it lands. We push the frontier and measure it.

How to apply

One page: the best AI system you built from the ground up and what it did in production, how you evaluated it, and the first thing you would change about how agentic systems are usually built. A link to code we can read beats any cover letter. 

We are looking for a Full Stack Engineer to join enterprise AI projects focused on intelligent automation, AI integrations, and modern internal platforms.
You’ll work on scalable web applications powered by Python, React, cloud technologies, and LLM-based features.

Requirements

• Strong experience with Python
• Experience with FastAPI or similar backend frameworks
• Commercial experience with React.js
• Experience building REST APIs and full stack applications
• Familiarity with cloud platforms (preferably Azure)
• Understanding of scalable architectures and software engineering best practices

Nice to Have

• Experience with AI/LLM integrations
• Familiarity with LangChain, LangGraph, or AI agent frameworks
• Experience with Azure OpenAI or similar AI services
• TypeScript, Docker, CI/CD experience
• Experience with async processing, queues, or event-driven systems

Responsibilities

• Develop and maintain full stack applications
• Build backend services and frontend interfaces
• Integrate AI capabilities into enterprise products
• Collaborate with engineering and product teams
• Contribute to architecture, deployment, and performance improvements

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

About the client and the role:
Our client is a software company that develops and distributes software for macOS and iOS. We are seeking a Research Scientist to client’s AI R&D team, who will be at the forefront of AI research and play a pivotal role in driving research in human-computer interaction (HCI), specifically focusing on understanding macOS apps and their interfaces, using the latest advancements in natural language processing, computer vision, multimodality research.

Responsibilities:
- Lead HCI-focused AI research projects from concept to implementation.
- Translate cutting-edge research into innovative solutions for macOS applications.
- Collaborate across teams to integrate AI advancements into client’s products.
- Communicate research results to internal and external audiences (scientific conferences)

Requirements:
- Experience in one or more of the following Machine Learning domains: Computer Vision, Natural Language Processing (NLP), Human-Computer Interaction (HCI).
- Strong knowledge of linear algebra, probability theory/statistics, and graph theory.
- Advanced prototyping skills and experience implementing algorithms from academic papers.
- Previous experience publishing academic papers.

Nice-to-Have Skills/Experience:
- Ph.D. in machine learning, AI, statistics, computer science or related technical fields
- Familiarity with the Apple ecosystem, including iOS/macOS SDK and Core ML.

What we offer:
- We are a Ukrainian company, and we stand with Ukraine against russian aggression.
- Your health always comes first.
- Flexible working hours.
- Space to grow both professionally and personally.
- Teams we are proud of.
- Time-off policy that covers life’s needs.
- Join social initiatives.

We are looking for a specialist who will be responsible for the development and maintenance of a complex AI-powered search system built on top of a large interactive knowledge base. The system includes tool calling and agentic behavior that interact with internal tools and multiple types of storage: vector databases for semantic search, document-oriented databases for full-text search, as well as relational and graph databases for lookup by specific identifiers. The search interface is built as an LLM-powered chat that supports natural language queries and returns well-grounded conclusions, recommendations, and links to primary sources.

The candidate will be responsible for designing and implementing the full LLM-powered search pipeline, including orchestration, reasoning, reranking, tool calling, handling tool outputs, validating results, and generating final user-facing responses.

We expect the candidate to:

• Have hands-on commercial experience working with vLLM in self-hosted environments, including model optimization on H100/H200 GPUs, with a solid understanding of GPU architecture principles and cache behavior under production workloads.
• Have a deep understanding of the RAG concept and a proven track record of successfully deploying similar solutions in production.
• Have worked with Haystack, LlamaIndex, LangChain, or similar frameworks, and understand the core differences between them.
• Have experience using MCP and working with LLMs that call external tools and services.
• Have strong Python skills, including architectural principles, design patterns, multithreading, asynchronous programming, modern testing practices, and experience operating systems under load (monitoring, memory optimization, telemetry).
• Have experience working with document-oriented, relational, and vector databases, including an understanding of index types and the appropriate use cases for each.

It would be a plus if you have:

• Experience building or maintaining knowledge bases, AI-powered reporting systems, or chatbots with large context windows.
• Experience training or fine-tuning LLMs or compact models for domain-specific tasks.
• Practical DevOps experience: CI/CD, Kubernetes, Helm, ArgoCD.
• Experience developing OCR- and Vision-Language-based systems for processing unstructured data.
• Experience in data engineering: understanding of ETL/ELT processes, working with orchestrators (Airflow, Temporal, Prefect), and distributed computing systems (Ray, Spark).
• Experience working with Elasticsearch, MongoDB, PostgreSQL, Qdrant/Milvus, Neo4j, and other database systems.
• Understanding of cybersecurity processes and modern approaches to data protection.

We offer a fully remote work format on our own product, which uses state-of-the-art technologies and is focused on the public sector. The company has the status of being critically important to the Armed Forces of Ukraine. If needed, we provide military deferment and the necessary office equipment.

––––––––––––––––––––––––––––Українською––––––––––––––––––––––––––––

Шукаємо фахівця, який буде відповідати за розробку та супровід комплексної AI-powered пошукової системи на основі великої інтерактивної бази знань. У системі реалізовано tool calling та agentic behaviour, що працюють із внутрішніми інструментами та різними типами сховищ: векторними базами для семантичного пошуку, документоорієнтованими для повнотекстового пошуку, а також реляційними та графовими базами для пошуку за конкретними ідентифікаторами. Інтерфейс пошуку побудовано у форматі LLM-powered чату, який підтримує запити natural language і повертає аргументовані висновки, рекомендації та посилання на першоджерела. 

У зону відповідальності кандидата входитиме проєктування та впровадження повного пайплайну LLM-powered пошуку, включно з orchestration, reasoning, reranking, tool calling, обробкою відповідей інструментів, валідацією результатів та формуванням фінальних відповідей для користувача. 

Ми очікуємо, що кандидат: 

• Має практичний комерційний досвід роботи з vLLM у self-hosted середовищах, включно з оптимізацією моделей на GPU H100/200, розумінням принципів GPU-архітектур та поведінки кешу під продакшн-навантаженням. 
• Глибоко розуміє концепцію RAG та має досвід успішного впровадження подібних рішень у продакшені. 
• Працював з Haystack / LlamaIndex / LangChain або аналогічними фреймворками та орієнтується в принципових відмінностях між ними. 
• Має досвід використання MCP та роботи з LLM, які викликають зовнішні інструменти й сервіси. 
• Володіє Python на високому рівні: архітектурні принципи, шаблони проєктування, багатопоточність, асинхронність, сучасні практики тестування, а також досвід експлуатації систем під навантаженням (моніторинг, оптимізація пам'яті, телеметрія). 

• Має досвід роботи з документоорієнтованими, реляційними та векторними базами даних, включно з розумінням типів індексів і відповідних сценаріїв застосування. 

   

Буде плюсом: 

• Досвід створення або підтримки баз знань, систем генерації звітів на базі AI, розробка чатботів із великим контекстним вікном.
• Досвід навчання або fine-tuning LLM чи компактних моделей для специфічних задач. 
• Практичний досвід у DevOps: CI/CD, Kubernetes, Helm, ArgoCD. 
• Досвід розробки систем на основі OCR та Vision-Language моделей для обробки неструктурованих даних. 
• Досвід у data engineering: розуміння ETL/ELT-процесів, робота з оркестраторами (Airflow, Temporal, Prefect) та системами розподіленого обчислення (Ray.io, Spark). 
• Досвід роботи з Elasticsearch, MongoDB, PostgreSQL, Qdrant/Milvus, Neo4j та іншими СУБД. 
• Розуміння процесів кібербезпеки та сучасних підходів до захисту даних. 
   

Пропонуємо повністю віддалений формат роботи над власним продуктом, який використовує найсучасніші технології та орієнтований на державний сектор. Компанія має статус критично важливої для Збройних сил України. За потреби надаємо бронювання та необхідну офісну техніку.

Our company is developing its own interactive knowledge base. Users can upload any volume of diverse data, after which the system automatically transforms it into structured, searchable content. To achieve this, we use a comprehensive ML pipeline involving OCR, vision-language models, document parsers, and multiple types of data storage. Search is implemented through an LLM-based chat interface that allows users to submit queries in natural language, retrieve relevant documents, and receive summarized findings and recommendations based on the uploaded materials.

We are currently looking for an ML/AI Engineer with deep experience working with unstructured content - primarily photographs and scanned document copies - who will strengthen our team and lead the development of pipelines and solutions for processing this type of data.

We expect the candidate to:

• Have broad experience working with unstructured data and have used both classical OCR systems (Tesseract, PaddleOCR, EasyOCR) and modern models (PaddleOCR-VL, DeepSeek-OCR, dots.ocr, Qwen3-VL). Understand the strengths and weaknesses of different architectures and the scenarios in which each is most effective.
• Have hands-on experience training and fine-tuning OCR and Vision-Language models.
• Understand algorithmic approaches to image enhancement, preprocessing, and filtering in order to improve downstream recognition quality.
• Have worked with Docling (including advanced backend tuning for parsing complex PDFs and tables), markitdown, Apache Tika, PyMuPDF, and other tools for document conversion and structuring. Be comfortable with processing layered and non-trivial PDF files.
• Have experience working with self-hosted models, including deploying and optimizing models in vLLM environments on H100-class GPUs and above, and understand the principles of GPU architecture and KV cache.
• Have strong Python programming skills, including architectural patterns, multithreading, asynchronous programming, modern testing practices, and experience operating services under high load (monitoring, telemetry, memory utilization).
• Have experience working with document-oriented, relational, and vector databases, and understand index types and the relevant use cases for each type of storage.

It would be a plus if you have:

• Experience working with Speech-to-Text or Video-to-Text pipelines.
• Experience in content analysis, including classification, summarization, and NER.
• Experience in data engineering: understanding of ETL/ELT processes, experience with orchestrators (Airflow, Temporal, Prefect), and distributed computing systems (Ray, Spark).
• Experience in DevOps: CI/CD, Kubernetes, Helm, ArgoCD.
• Experience working with Elasticsearch, MongoDB, PostgreSQL, Qdrant/Milvus, Neo4j, and other storage systems.
• Understanding of modern cybersecurity approaches.
• Hands-on experience with production-grade RAG-based systems.

We offer a fully remote work format on our own product, which uses state-of-the-art technologies and is focused on the public sector. The company has the status of being critically important to the Armed Forces of Ukraine. If needed, we provide military deferment and the necessary office equipment.

–––––––––––––––––––––––––––––Українською–––––––––––––––––––––––––––––

Наша компанія розробляє власну інтерактивну базу знань. Користувачі можуть завантажувати будь-який обсяг різноманітних даних, після чого система автоматично перетворює їх на структурований, придатний для пошуку контент. Для цього застосовується комплексний ML-пайплайн із залученням OCR, VL моделей, документних парсерів та різних типів сховищ даних. Пошук реалізовано через чат на базі LLM, який дозволяє формувати запити звичайною мовою, отримувати релевантні документи, а також узагальнені висновки та рекомендації на основі завантажених матеріалів. 

Зараз ми шукаємо ML/AI інженера з глибоким досвідом роботи з неструктурованим контентом (насамперед фотографіями та скан-копіями документів), який підсилить нашу команду та очолить напрям розробки пайплайнів і рішень для обробки таких даних. 

Ми очікуємо, що кандидат: 
• Має широкий досвід роботи з неструктурованими даними та застосовував як класичні OCR-системи (Tesseract, PaddleOCR, EasyOCR), так і сучасні моделі (PaddleOCR-VL, DeepSeek-OCR, dots.ocr, Qwen3-VL). Розуміє сильні та слабкі сторони різних архітектур і сценарії їх застосування. 
• Має практичний досвід навчання та fine-tuning OCR і Vision-Language моделей. 
• Розуміє алгоритмічні методи покращення зображень, їх попередню фільтрацію та обробку для підвищення якості подальшого розпізнавання. 
• Працював з Docling (включно з тонким налаштуванням бекендів для парсингу складних PDF і таблиць), markitdown, Apache Tika, PyMuPDF та іншими інструментами для конвертації й структуризації документів. Орієнтується у процесингу багатошарових і нетривіальних PDF-файлів. 
• Має досвід роботи з self-hosted моделями, зокрема запуску та оптимізації моделей у середовищах vLLM на GPU рівня H100 і вище, розуміє принципи GPU-архітектури та KV cache. 
• Має сильні навички програмування на Python: архітектурні патерни, багатопоточність, асинхронність, сучасні практики тестування, досвід експлуатації сервісів під високим навантаженням (моніторинг, телеметрія, утилізація пам'яті). 
• Має досвід роботи з документоорієнтованими, реляційними та векторними базами даних, розуміє типи індексів і відповідні юзкейси для кожного типу сховищ. 

Буде плюсом: 
• Досвід роботи з Speech-to-text або Video-to-text пайплайнами.
• Досвід аналізу контенту: класифікація, сумаризація, NER. 
• Досвід у data engineering: розуміння ETL/ELT-процесів, робота з оркестраторами (Airflow, Temporal, Prefect) та системами розподіленого обчислення (Ray.io, Spark). 
• Досвід у DevOps: CI/CD, Kubernetes, Helm, ArgoCD. 
• Досвід роботи з Elasticsearch, MongoDB, PostgreSQL, Qdrant/Milvus, Neo4j та іншими сховищами. 
• Розуміння сучасних підходів до кібербезпеки. 
• Практичний досвід із системами на основі RAG у продакшені. 

Пропонуємо повністю віддалений формат роботи над власним продуктом, який використовує найсучасніші технології та орієнтований на державний сектор. Компанія має статус критично важливої для Збройних сил України. За потреби надаємо бронювання та необхідну офісну техніку.

Responsibilities:

• Own and support core ML infrastructure (MLflow, CVAT, GPU/CPU cloud instances, internal data tools).
• Assist Data Engineers with deploying and maintaining data pipelines.
• Act as the primary contact for ML and Data teams on infra-related requests.
• Improve reliability and efficiency by adding automation, monitoring, and tooling enhancements.
   

Must have:

• Knowledge of Python, Terraform, Docker, Linux.
• Hands-on experience with cloud environments (Azure prefered).
• Understanding of the end-to-end ML lifecycle (data → training → evaluation → deployment → monitoring).

Nice to have:

• Familiarity with Kubernetes.
• Experience with FFmpeg or GStreamer for video processing and streaming.
• Experience with model serving frameworks and inference formats (ONNX, TensorRT, OpenVINO, NVIDIA Triton).
• Experience deploying computer vision models, especially to edge/embedded devices (NVIDIA Jetson, Intel platforms, Raspberry Pi).

• Надаємо бронювання від мобілізації

About the role

We're building an on-premise AI platform that processes large volumes of multilingual documents and runs autonomous agents that gather and analyse information from open sources. Everything runs on our own hardware: self-hosted models, no external APIs.

You'll own the agentic layer: the LangGraph applications that plan, retrieve and reason, the prompt engineering behind them, and the evaluation that keeps their quality measurable.

What you'll work on

• Multi-step agents on LangGraph: planning loops, tool use, checkpointing, crash recovery, human-in-the-loop
• Prompt engineering with schema-constrained decoding, where every response is validated against a contract
• RAG pipelines: chunking, hybrid dense + sparse retrieval, citation-grounded generation
• Information extraction from documents into structured records
• Evaluation: gold sets, regression gates, scenario tests over full agent trajectories
• Prompt-injection defence: policy layers over tool calls, adversarial test suites

Stack

Python, LangGraph, FastAPI, vLLM, Qdrant, OpenSearch, Postgres, Kubernetes, MLflow, inspect_ai

What we're looking for

• 4+ years in Python, with at least 1 year building LLM-based systems in production
• Hands-on experience with an agent framework (LangGraph, Pydantic AI, or similar), something you've shipped rather than prototyped
• Solid RAG experience: you understand why retrieval quality, not model choice, is usually the bottleneck
• Practical prompt engineering: structured outputs, evaluation-driven iteration
• Experience with self-hosted inference (vLLM, TGI, Ollama)
• Strong engineering discipline: typed code, tests, reproducibility

Nice to have

Document processing at scale (OCR, layout extraction) · information extraction or knowledge graphs · LLM evaluation tooling · air-gapped environments · web crawling with robots compliance and rate limiting

• Project Description:

  Design, develop, test, and deploy enterprise AI agents, copilots, and multi-agent systems.

   

• Responsibilities:

  Build agents using Azure AI Foundry, Copilot Studio, or other approved frameworks, including Python-based frameworks where appropriate.
  Build or coordinate rapid prototypes and validate them with business users.
  Develop prompts, instructions, tool definitions, routing, and orchestration workflows.
  Implement grounding, citation, retrieval, context management, guardrails, and human-review patterns.
  Integrate agents with enterprise tools and APIs.
  Create automated evaluations and optimize answer quality, latency, reliability, observability, and cost.
  Support production troubleshooting, monitoring, feedback loops, and continuous agent improvement.

   

• Mandatory Skills Description:

  Bachelor's degree in Computer Science, Software Engineering, AI, or a related field.
  5+ years of software development experience.
  2+ years developing generative AI, AI assistant, or agent-based applications.
  Strong Python skills and experience integrating REST APIs and cloud services.
  Experience deploying secure, testable enterprise applications.

   

• Nice-to-Have Skills Description:

  Experience with Azure AI Foundry and Copilot Studio.
  Experience with Semantic Kernel, MCP, multi-agent systems, or comparable orchestration frameworks.
  Experience implementing RAG, evaluation pipelines, citations, and tool-use controls.
  Azure AI Engineer or related certification

   

• Languages:
  • English: B2 Upper Intermediate