Jobs ML / AI Engineer

Місія ролі:
Розробляти прикладні AI-продукти та сервіси: backend, frontend, API, інтеграції з LLM/ML models, RAG-пайплайни, workflow automation, evaluation logic та production-ready функціональність для користувачів. Це роль не дослідника Data Science, а інженера, який перетворює AI capability у працюючий продукт.

Обов’язки:
-Розробка AI-enabled web applications та internal tools.
-Побудова backend API для AI-сервісів.
-Інтеграція LLM, embeddings, vector search, document pipelines.
-Реалізація RAG/workflow/agent-like сценаріїв там, де це виправдано.
-Побудова UI для користувачів, операторів, аналітиків або адміністраторів.
-Впровадження access control, audit trail, security controls.
-Підготовка технічних прототипів і доведення їх до production-ready стану.
-Спільна робота з BA над уточненням вимог.
-Спільна робота з DevOps/MLOps щодо deployment та monitoring.
-Спільна робота з AppSec щодо threat modeling і security testing.

Вимоги:
2+ роки software engineering досвіду;
Досвід backend-розробки на Python, Go, Java, Node.js або .NET.
Досвід frontend-розробки: React, Next.js, Angular або Vue.
Практичний досвід з REST/gRPC API, authN/authZ, role-based access, audit logging.
Досвід з PostgreSQL або іншими relational DB.
Розуміння асинхронної обробки: queues, workers, schedulers, event-driven flows.
Практичний досвід інтеграції з LLM API або open-source LLM.
Вміння писати тести, робити code review, працювати з CI/CD.
Бажано:
Досвід RAG: embeddings, vector DB, chunking, retrieval, reranking, source attribution.
Досвід з LangChain, LlamaIndex, Haystack або власними lightweight orchestration-підходами.
Досвід з LLM evaluation: golden datasets, regression tests, hallucination checks, prompt/version control.
Досвід з document processing: OCR, PDF parsing, structured extraction.
Досвід із secure coding та OWASP ASVS/API Security.
Досвід побудови internal tools, dashboards, workflow automation.

 

Умови для чинних військових:
-Військова служба
-Служба в Києві (житло не надається), Центр, Київ
-Повне грошове забезпечення для тилової посади

Умови для цивільних:
-Офіційне працевлаштування
-Офісна робота, Київ, Центр
-Можливе бронювання

Senior AI Engineer needed to design and ship AI products for a healthcare company that enables remote patient monitoring, clinical research, and personalized healthcare. You'll build generative-AI workflows, clinical decision support, agents, and retrieval over health data — along with the evaluation and guardrails that make them safe to use in a clinical setting. You'll own these products end to end and work across teams, partnering closely with clinicians, product, and engineering to identify where AI genuinely improves a patient or clinician outcome — and explaining the trade-offs clearly to both technical and non-technical people. This is a hands-on senior role for an AI builder with strong software fundamentals who wants to own the outcome of an AI product, not just the code.

What You'll Do:

• Take AI products from problem framing to production — scope the use case, choose the approach (generative AI, classical ML, retrieval, agents, or a hybrid), and ship it
• Design prompts, tools, and retrieval strategies that work reliably in a clinical context, with structured outputs and clear failure modes
• Build the services and APIs that expose AI capabilities to our products, with real attention to latency, cost, and observability
• Shape the data behind each product — sources, schemas, labelling, and quality checks — and build the pipelines that prepare it for training, evaluation, and inference
• Define how quality is measured (offline metrics, golden sets, human review, and live monitoring) and use the results to drive iteration
• Build guardrails, content checks, and graceful degradation paths suited to healthcare use cases
• Operate AI products in production — monitor model behaviour, catch drift and regressions, and run incident response when needed
• Write production-quality code, tests, and documentation, review others' work to the same standard, and use AI-assisted development tools to move faster without compromising quality
• Partner with clinical, product, and engineering colleagues to decide where AI is — and isn't — the right answer, keeping everyone aligned and clearly communicating AI trade-offs to technical and non-technical stakeholders

• Mentor other engineers on applied AI — evaluation, generative-AI design patterns, and shipping models responsibly

   

What We're Looking For:

• 5+ years of professional software engineering experience, with meaningful time building and shipping AI/ML products in production
• A track record of owning an AI product from problem statement to launch — including model/approach selection, evaluation, and iteration based on real usage
• Hands-on experience with generative-AI systems: prompting, tool/function calling, retrieval-augmented generation (RAG), multi-step agents, structured output, and handling failure modes
• Solid grounding in core machine learning (supervised learning, embeddings, evaluation, overfitting, data quality), with the judgment to know when to use classical ML, fine-tuning, or off-the-shelf models — and when AI isn't the answer at all
• Strong production engineering skills — clean, well-tested code, sensible API design, and the ability to operate your own services (Python is our default)
• Comfort with the data side of an AI product — ingesting, cleaning, and modelling structured and unstructured data, and designing pipelines for training, evaluation, and inference
• An evaluation mindset — defining offline and online metrics, building golden sets or human-review loops, and catching regressions before users do
• Clear, confident written and verbal communication — you can explain AI trade-offs to an engineering stand-up and to a clinical or executive stakeholder in the same day, and adapt your style to each
• An ownership and product mindset — you care whether the model and the product are doing the right thing for users, you stay accountable after launch, and you balance capability, cost, latency, safety, and complexity for the stage of the product
• Confident with Git, code review, automated testing, and CI/CD

Nice to Have:

• Production experience with vector databases, embedding pipelines, and retrieval-quality tuning
• MLOps practices — model versioning, evaluation harnesses, shadow deployments, and prompt/dataset management
• Experience with cloud AI platforms (Azure AI, AWS Bedrock/SageMaker, GCP Vertex) and deploying inference on AWS, GCP, or Azure
• Familiarity with fine-tuning, distillation, or hosting open-weight models when economics or privacy call for it
• Background in healthcare, life sciences, or another regulated, data-sensitive domain (HIPAA, GDPR, ISO 27001, medical-device software)
• Experience with FHIR, HL7, or other healthcare interoperability standards

What Success Looks Like:

• AI products that ship and make a measurable difference — adoption, task completion, and clinical or operational outcomes where defined
• Healthy AI behaviour in production — strong evaluation scores, a high regression catch rate, and safety incidents avoided
• Reliable, observable AI services with healthy uptime, latency, and cost for the surfaces you own
• Predictable delivery within agreed scope and timelines
• Engineers you mentor or pair with growing and becoming more effective

On behalf of our client, we are looking for a Senior AI Engineer

Requirements:

- 5+ years of experience in AI Engineering, Data Engineering, or Applied ML Infrastructure

- Expert-level Python

- Strong experience with distributed processing frameworks (Apache Beam preferred; Spark/Flink acceptable)

- Hands-on experience integrating LLMs in production (prompting, orchestration, provider abstraction)

- Experience with graph databases (Dgraph, Neo4j, JanusGraph, or similar)

- Strong understanding of distributed systems, parallel processing, and large-scale data pipelines

- Solid SQL and data modeling fundamentals

- Experience with Docker and containerized deployments

- Experience with cloud platforms (GCP preferred, AWS/Azure acceptable)

- Strong backend/system design mindset (performance, scalability, reliability)

- Ability to work independently and take ownership

- Strong problem-solving skills in complex environments

- English level: B2+ 

- Availability to overlap with US business hours

Company offers:

- Long-term employment with possibilities for professional growth

- Fully remote work

- Reasonably flexible schedule

- 15 days of paid vacation

- Regular performance reviews

Тренувати, доводити до продакшну та інтегрувати моделі для
аналізу аудіо та текстової інформації в шумних, реальних
умовах.

ПРО ПРОЄКТ:
Ми будуємо ML-платформу для аналізу аудіо та текстової інформації — з
високою зашумленістю, складними акустичними умовами та великою
різноманітністю лексики. Це повний life-cycle: збір та підготовка даних,
тренування моделей (ASR, VAD, класифікація тексту, NER, ембеддинги),
експорт у продакшн (ONNX / TensorRT / Triton) та інтеграція у потокові
пайплайни (Apache Flink, RabbitMQ, Kafka). Кінцеві споживачі — аналітики,
які працюють з великими обсягами інформації; тому якість моделей та
якість їх інженерної реалізації одинаково критичні.

ЧИМ ВИ БУДЕТЕ ЗАЙМАТИСЯ
Тренувати та доводити до продакшну моделі у трьох суміжних доменах:

• ASR Speech-to-text — fine-tuning сучасних архітектур (CTC, RNN-T, TDT, attention-based) на шумних аудіо. PyTorch Lightning, NeMo, Hydra-конфіги, multi-GPU.
• VAD Voice activity detection — fine-tuning рекурентних та CNN-based моделей на спектрограмах, threshold optimization, ROC-аналіз.
• NLP / IR Текст та пошук — multi-label класифікація на трансформерах та класичних методах, NER, дедуплікація тексту (MinHash LSH) та аудіо (contrastive embeddings, SimCLR), векторний та повнотекстовий пошук.

— Експортувати моделі в ONNX, готувати TensorRT-плани (FP16, optimization profiles, dynamic shapes), інтегрувати в NVIDIA Triton (ensemble, BLS, dynamic batching, instance groups).
— Будувати продакшн-сервіси: FastAPI-фронти, асинхронні воркери на RabbitMQ / Kafka / S3 / Redis, інтеграція в Flink-пайплайни.
— Проводити ретельні бенчмарки: WER, F1 per-class, ROC-AUC, RTFx, throughput, A/B-тести з людьми розмітниками (Prodigy).

— Читати papers, відтворювати state-of-the-art підходи та адаптувати їх під наші дані.

— Версіонувати дані та моделі (DVC + MinIO/S3), трекати експерименти (Weights & Biases), писати тести (pytest), проходити code review.

ОБОВʼЯЗКОВІ ВИМОГИ

• 5+ років комерційного досвіду в ML / NLP / Information Retrieval. CV-досвід не потрібен.
• Python як основна мова — впевнене володіння сучасним Python (3.11+), типізація, async/await, написання чистого підтримуваного коду.
• PyTorch на рівні автора кастомних training loops — не лише Trainer.fit(), а й написання своїх loop-ів, distributed training (DDP, gradient accumulation, mixed precision), розуміння як працюють loss-функції під капотом (CTC, RNN-T, contrastive losses).
• Принаймні один глибокий досвід в одному з напрямків: ASR / sequence-to-sequence, класичний NLP на трансформерах та класичних методах, або Information Retrieval (BM25, dense retrieval, MinHash / LSH, ранжування).
• Здатність читати та відтворювати papers — брати модель або підхід з arXiv, розуміти trade-offs, адаптувати під свої дані, а не лише запускати готові скрипти.
• Інженерія продакшну — досвід доведення моделі від ноутбука до сервісу: експорт (ONNX / TorchScript), оптимізація інференсу, побудова REST / gRPC API, написання тестів (unit + integration).
• HuggingFace екосистема — впевнена робота з transformers, datasets, tokenizers, accelerate, evaluate, hub; досвід fine-tuning, custom training loops, кастомних токенайзерів (BPE, SentencePiece, WordPiece).
• Класичний ML — sklearn, XGBoost, threshold tuning для imbalanced multi-label задач, метрики Precision / Recall / F1 (macro/micro/per-class), ROC-AUC.
• Git, pre-commit, лінтери, CI — сприймати ці речі як норму, а не як перешкоду. Англійська — читання технічної документації та papers без труднощів.

БУДЕ ПЛЮСОМ

• NVIDIA Triton Inference Server — ensemble, BLS, Python backend, config.pbtxt.
• TensorRT — компіляція engine, optimization profiles, INT8/FP16 квантизація.
• ONNX Runtime — оптимізація графів, провайдери (CUDA / TensorRT / CPU).
• Data engineering — Apache Flink, Kafka, потокова обробка, DVC, data pipelines.
• Інфраструктура — Docker, Kubernetes, Helm, ArgoCD, GitLab CI, AWS (S3, CodeArtifact).

СТЕК, З ЯКИМ ВИ БУДЕТЕ ПРАЦЮВАТИ:
CORE Python 3.11+, uv, PyTorch, PyTorch Lightning, HuggingFace (transformers, datasets, tokenizers, accelerate), spaCy, sentence-transformers, sklearn, XGBoost.

ІНФЕРЕНС ONNX Runtime, TensorRT, NVIDIA Triton; FastAPI, Uvicorn, asyncio, Pydantic v2.

ДАНІ RabbitMQ, Kafka, Apache Flink; S3 / MinIO, Elasticsearch, PostgreSQL, Redis Stack.

MLOPS DVC, Weights & Biases, GitLab CI/CD, Kubernetes, ArgoCD; jiwer (WER), torchmetrics, scikit multilearn, Prodigy.

ЯК МИ ПРАЦЮЄМО:

• Маленька команда, низька бюрократія; рішення приймає інженер, який володіє контекстом.
• Кожна зміна моделі або інфраструктури має чіткі бенчмарки до/після — ми не «відчуваємо що краще», ми міряємо.
• Production-mindset з самого початку: будь-яка модель з ноутбука повинна мати план виходу на сервер.
• Документація, діаграми архітектур, чесні post-mortem-и, культура writing-it-down.

We are expanding our Advanced AI Solutions team and building a core group of top-tier talent to anchor our upcoming Claude Enterprise Practice.

We are looking for 10 exceptional tech professionals—including Delivery Leads, Solution Architects, Tech Leads, and Senior AI Engineers—to join us for an exclusive, fully-funded upskilling initiative.

💡 What We Offer:

• Free Corporate Access: Get enrolled directly into the premium Anthropic Academy.
• Financial Incentive: Receive a guaranteed bonus of 5,000 UAH immediately upon successful completion of the CPN learning path.
• Elite Certification: Upon finishing, we will unlock and sponsor your official technical examination to become a Claude Certified Architect Foundations (CCAF) specialist.
• Career Progression: Top performers who complete the track will be prioritized for high-impact, premium enterprise customer engagements and leadership roles in our expanding practice.

🎯 Who We Are Looking For:

• Delivery Leads / Technical Project Managers who can steer complex AI engagements.
• Solutions Architects / Tech Leads with a background in Cloud/AI infrastructure.
• Engineers with solid experience in LLMs, Prompt Engineering, or system integration.
• Upper-Intermediate to Advanced English (all learning materials and certifications are in English).
• Dedication to complete the learning path within the next [X weeks / e.g., 2 weeks].

This is a rare opportunity to get certified directly by one of the world's leading AI organizations at zero cost to you, while getting paid for your learning time.

📥 How to Apply: 

complete the CPN learning path

Link upon request.
Indicate our company:

QRoot.app

Complete 4 courses.
Report your success.

Receive our payment of 5,000 UAH.

We are looking for a Middle AI/ML Engineer to develop and enhance AI-driven solutions within the Palantir Foundry and AIP ecosystem.

In this role, you will focus on building and iterating on machine learning and LLM-based solutions, integrating them into Foundry workflows to support analytics, automation, and decision-making. You will collaborate closely with data engineers, business analysts, and domain experts to deliver practical, production-ready AI solutions.

Key Responsibilities:

• Develop and enhance machine learning and AI models to support predictive analytics, classification, forecasting, and AI-assisted workflows.
• Build AI and ML solutions within Palantir Foundry, using Python and existing Foundry pipelines, Ontology objects, and workflows.
• Apply LLMs and NLP techniques (e.g. prompt engineering, fine-tuning, embeddings, retrieval-augmented workflows) using Palantir AIP for enterprise use cases.
• Collaborate with data engineers to understand data sources, ensure data quality, and prepare datasets for model training and inference.
• Conduct experiments, evaluate model performance, and iterate on features and model approaches.
• Integrate AI models into Foundry workflows to surface insights and support business processes.
• Support model deployment and monitoring by following established team standards and best practices.
• Work closely with business and domain stakeholders to translate requirements into practical AI-driven solutions.
• Document model behavior, assumptions, and limitations to support transparency and compliance.
• Stay up to date with applied AI and GenAI trends and contribute ideas under guidance from senior team members.

Requirements:

• 3+ years of experience in machine learning, AI engineering, or applied data science.
• Strong Python skills; experience with ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Hands-on experience with LLMs, NLP, or GenAI use cases (e.g. prompt design, embeddings, text classification, summarization).
• Practical understanding of the ML lifecycle: data preparation, feature engineering, model training, evaluation, and iteration.
• Experience working with structured data (tabular, time series); exposure to text or unstructured data is a plus.
• Familiarity with enterprise data environments and collaborative development workflows.
• Ability to clearly explain model results and AI behavior to non-technical stakeholders.
• Upper-Intermediate English or higher.

Nice to have: 

• Proficiency in Foundry Ontology, Object Builders, and Code Repositories.
• Experience in big pharma or highly regulated industries.
• Knowledge of data privacy, compliance, and security best practices in AI applications.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).

We offer*:

• Flexible working format - remote, office-based or flexible
• A competitive salary and good compensation package
• Personalized career growth
• Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more)
• Active tech communities with regular knowledge sharing
• Education reimbursement
• Memorable anniversary presents
• Corporate events and team buildings
• Other location-specific benefits

*not applicable for freelancers

AI Product Engineer / AI-First Developer

We are looking for an AI-first engineer who can leverage modern AI tools to dramatically accelerate software development.

Our goal is not simply to integrate AI into products, but to rethink the development process itself.

You will work on rapid prototyping, MVP development, internal automation, AI-powered products, and client projects where development speed and efficiency are critical.

Responsibilities:

• Build software products using AI-assisted development workflows
• Use tools such as Claude Code, Cursor, OpenAI, Anthropic and modern AI ecosystems
• Rapidly prototype and validate product ideas
• Design and implement AI-powered features
• Build integrations with LLMs and AI services
• Optimize development processes through AI automation
• Collaborate with engineering and product teams

Requirements:

• Strong software engineering background
• Commercial experience with Python, JavaScript/TypeScript or similar technologies
• Experience using AI coding tools in production workflows
• Experience with OpenAI, Anthropic or other LLM APIs
• Understanding of prompt engineering and AI agent workflows
• Ability to quickly learn new technologies
• Strong problem-solving skills

Nice to have:

• Experience with RAG systems
• Experience with LangChain, LangGraph or LlamaIndex
• Experience building AI agents
• Startup or MVP development experience

Most importantly, we are looking for engineers who can demonstrate how AI helped them significantly increase development speed and productivity.

PwC is a global network of more than 370,000 professionals in 149 countries that turns challenges into opportunities. We create innovative solutions in audit, consulting, tax and technology, combining knowledge from all over the world.

PwC SDC Lviv, opened in 2018, is part of this global space. It is a place where technology is combined with team spirit, and ambitious ideas find their embodiment in real projects for Central and Eastern Europe.

What do we guarantee?

• Work format: Remote or in a comfortable office in Lviv - you choose.
• Development: Personal development plan, mentoring, English and Polish language courses.
• Stability: Official employment from day one, annual review of salary and career prospects.
• Corporate culture: Events that unite the team and a space where everyone can be themselves.

Join our dynamic AI team and be at the forefront of innovation, working on transformative projects for some of the most recognized companies worldwide. As an AI Consultant, you will participate in the implementation and development of AI-powered solutions across a broad range of applications, from Gen AI tools to data analytics fraud detection systems.

Key Responsibilities:

• Participate in AI projects from inception to completion, ensuring timely delivery of high-quality solutions;
• Solution Development: Design and implement cutting-edge AI and GenAI solutions, including Retrieval-Augmented Generation (RAG) for knowledge retrieval, prompt optimization, and fine-tuning of large language models (LLMs);
• Apply AI techniques to areas such as fraud detection, satellite imagery analysis, time-series forecasting, and automated data quality validation to enhance decision-making and automation;
• Develop web applications (FE / BE / cloud architecture) enabling users to interact with AI systems;
• Client Interaction: Work closely with clients to understand their unique needs and provide expert guidance and support throughout the project lifecycle.

Requirements and skills:

• 2+ years of professional experience in this or similar area;
• Experience working on AI/ML projects, in areas such as Generative AI, NLP, Computer Vision or Machine Learning model development and deployment;
• University degree in Mathematics or a similar field is an advantage;
• Programming skills in Python (pandas, plotly, numpy);
• Experience with Full stack development (JavaScript) is a significant plus.
• Experience with SQL databases; familiarity with NoSQL is a plus;
• Experience with generative AI models is a plus;
• Fluent in English (level B2 or higher).

Policy statements:
https://www.pwc.com/ua/uk/about/privacy.html

About the Role:
We're looking for a forward-deployed AI engineer to embed with the existing analyst team and transform how they work with LLMs - day to day, in practice. This is as much an elicitation and communication role as an engineering one: you'll sit with the analysts, learn their workflows deeply, and turn that understanding into working AI-powered tooling.
The immediate priority is connecting our models (Claude and Gemini) to external data sources via MCP, leveraging existing servers where available, writing new ones where needed, and building practical agentic tools and research "skills" the team can use immediately. This will feed into a broader LangGraph-based platform already under development, but extracting real value for our analysts now comes first.

What You'll Do:
- Embed with analysts to understand their investigative workflows and translate them into working tooling.
- Connect LLMs to external/third-party databases and internal resources via MCP.
- Build practical agentic tools and research skills across Claude and Gemini.
- Advise on best use of our current models and agent architecture.
- Review and provide recommendations on existing internal development efforts.
- Help define how LLMs integrate into the team's daily work — before automating for external-facing systems.

What We're Looking For:
- Forward-deployed / solutions engineering experience, a track record of embedding with non-technical specialists and shipping tools for them. This is the most important qualification.
- Hands-on experience with both Claude and Gemini: tool/function calling, agentic patterns, MCP.
- Comfort treating prompt engineering and agent/skill design as core work, not a side task.
- Strong security awareness.
- Excellent English communication skills (written and verbal).
- Python proficiency (FastAPI, async patterns).

Nice to have:
- Familiarity with LangGraph is a plus 
- React/TypeScript familiarity.

About the Client
This is a specialist intelligence and investigations firm whose analysts conduct deep-dive research into complex entities: individuals, organisations, and networks, etc. for clients operating in high-stakes environments: due diligence, compliance, litigation support, and financial crime. The work is detail-intensive and source-critical: every finding must be traceable, verifiable, and defensible. They are now building AI infrastructure to support and accelerate that process - not to replace human judgement, but to give their analysts better tools.

Stack: Python 3.10+, FastAPI, LangGraph, Pydantic | React, TypeScript, Vite, Zustand.

We are looking for an Autonomous systems engineer with a background in computer vision, control theory and mathematical modelling. The ideal candidate will have a strong math background, experience with autonomous systems, modelling and control design. 

Required Qualifications: 

• 2 years of hands-on experience with classical computer vision
• Understanding of geometrical computer vision principles 
• System identification and control
• Sensor fusion/signal processing
• Proficiency in C++ and/or experience with Python
• Experience with Linux and single-board computers
• Experience with PID controllers tuning in embedded or autonomous systems
• Ability to quickly navigate through recent research and trends in computer vision and autonomous systems
• Relevant work experience or education in STEM field

Nice to Have:

• Experience with SLAM and/or Visual-Inertial Odometry (VIO)
• Matlab
• Experience with PX4, betaflight, Ardupilot 
• Familiarity with neural networks and common CV frameworks/libraries (OpenCV, NumPy, PyTorch, ONNX, Eigen, etc.)
• Experience with simulation tools (Gazebo, AirSim)
• SITL, HITL testing
• ROS2

This is a fantastic opportunity to join a fast-growing company at the heart of the digital revolution. Our software product is revolutionising manufacturing through automation. We are trusted by global brands and work across sectors (mobility, consumer goods, machinery etc).

We have a talented and ambitious team. We value integrity and are passionate about our mission to revolutionise advanced manufacturing.

This is your opportunity to:

• Be part of an AI product company in advanced manufacturing.
• Be part of a research focused team.

Responsibilities: 

• Research, test and develop generative AI based solutions.
• Collaborate with mechanical engineers to enhance your research.
• Design and implement end-to-end AI solutions that integrate with existing systems.
• Collaborate with software engineers and product teams to understand business needs and translate them into technical solutions. 
• Research emerging AI trends and techniques to continually improve the AI solution offerings. 
• Prepare and maintain documentation on design, code, and processes to ensure organizational knowledge retention. 
• Monitor and evaluate the performance of AI systems in production, identifying areas for improvement or further development.

You will be joining a well-versed team if you have:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field. 
• Proven experience in developing generative AI or machine learning models.
• Commercial experience in using large language models.
• Programming skills in languages such as Python or JavaScript.
• Familiarity with databases (SQL, NoSQL). 
• Solid understanding of deep learning, NLP, computer vision, or other specialized areas within AI is a plus. 
• Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.
• Good communication skills and the ability to translate complex technical concepts to non-technical stakeholders. 
• Commitment to continuous learning and development in the field of AI and emerging technologies.

EXITEK team is looking for a Machine Learning Engineer. We're expanding our development team and looking for a passionate, experienced, self-organised candidate to work on our BeautyUp app.

Responsibilities:

• Design, develop, and optimize state-of-the-art computer vision pipelines, algorithms and models;
• Conceive and experiment with innovative features and functionalities in the realm of photo editing and enhancement;
• Collaborate with cross-functional teams to integrate machine learning models into production systems and pipelines.

Requirements:

• Proficiency in Python and PyTorch;
• Experience in Computer Vision (GANs, SD, etc.) is a must;
• Deep understanding of the current state of the art in machine learning and computer vision;
• Robust communication skills, complemented by the ability to thrive in a collaborative team environment.

Role overview:

We are looking for a practical security engineer who can improve detection quality, investigate real incidents, and help reduce manual SOC work.

This role is not about building an “AI security platform”. The focus is on better visibility, cleaner detections, faster investigations, and useful automation.

What You Will Work On:

Threat Hunting

Run targeted hunts based on clear hypotheses, including:

• Suspicious cloud activity
• Abnormal IAM or API usage
• Unusual network or application behavior
• Weak or missing detection coverage
• Gaps in logging and telemetry

You will use internal data and external threat intelligence to validate risks and improve detection logic.

Security Incident Investigation

Investigate security alerts and incidents end-to-end:

• Understand what happened
• Validate whether the alert is real or noisy
• Identify affected users, systems, and data
• Collect evidence from logs and infrastructure
• Document findings clearly
• Recommend fixes or detection improvements

Expected experience: 2+ years of hands-on security incident investigation.

Detection Engineering

Improve existing detections and create new ones where needed:

• Reduce false positives
• Improve signal quality
• Tune alerts based on real data
• Validate whether detections catch meaningful activity
• Map useful detections to MITRE ATT&CK where appropriate.

Automation and Internal Tooling

Build small but useful tools to reduce repetitive work:

• Scripts for log analysis
• Alert enrichment
• Investigation helpers
• Data normalization
• Tool integrations
• Basic SOAR-style automation

Practical AI / LLM Usage

Use LLMs only where they bring practical value:

• Alert enrichment
• Investigation summaries
• Internal tooling
• Analyst productivity

No hype. No AI-first positioning. AI is just one tool in the workflow.

Requirements:

• Solid experience with SIEM tools, such as: Elastic, Splunk, Microsoft Sentinel, Similar platforms
• 2+ years investigating real security incidents
• Strong understanding of: - AWS logs and IAM activity - Cloud security basics - Network fundamentals - APIs, authentication flows, and modern application behavior
• Comfortable working with: Large datasets; Raw logs; Noisy alerts; Incomplete telemetry
• Python or similar language for data processing
• Experience with automation: SOAR, Scripts, Internal tools, Custom workflows
• Basic DevOps knowledge: - AWS - Linux - Docker - Kubernetes basics - CI/CD basics - Cloud infrastructure concepts - Logs, metrics, and monitoring.

Nice-to-have (but not critical):

• Experience applying simple ML or statistical methods to logs
• Familiarity with LLMs in internal tooling
• Experience with data pipelines (Kafka, Spark, Similar systems)
• MITRE ATT&CK mapping
• Detection-as-code experience
• Understanding of infrastructure monitoring (Prometheus, Grafana, OpenTelemetry)

What will make you effective here:

• You do not trust alerts blindly
• You care about signal quality, not the number of detections
• You can explain why a detection is noisy or useless
• You understand systems, not just security tools
• You are comfortable reading raw logs
• You prefer building automation once instead of repeating manual work
• You can work with engineers and explain security findings in practical terms

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

We’re focusing on:

• Complex document processing pipelines
• Knowledge retrieval systems powered by LLMs and RAG
• Building production-ready GenAI solutions with real business value

I’m open to candidates of different experience levels — what matters most is strong problem-solving skills, curiosity, and hands-on experience.

You should have:

• Solid understanding of machine learning and NLP algorithms
• Practical knowledge of how LLMs and Retrieval-Augmented Generation (RAG) work

• Previous experience with real-life AI/ML projects (commercial) - at least 1 year