AI Engineer (M/F/D)
⚲ Warszawa
Do uzgodnienia
Wymagania
- AI
- Python
- NLP
- Large Language Models
- GenAI
- MLflow
- Google Cloud Platform
- RAG
- Kedro
- Kubernetes
Opis stanowiska
Key responsibilities
• Independently deliver scoped AI, GenAI, NLP, and RAG features of moderate complexity
• Develop and enhance RAG pipelines, including document parsing and ingestion, chunking and metadata strategies, query transformation, retrieval and ranking, response generation and grounding
• Build GenAI features using LLM APIs, structured prompting, and orchestration frameworks (e.g., LangChain, LangGraph, DSPy, etc.)
• Evaluate AI system performance using practical methods such as retrieval metrics, response quality assessment, hallucination analysis, latency measurement, cost analysis, and failure-case testing
• Understand engineering trade-offs across model quality, latency, cost, reliability, maintainability, and implementation complexity
• Own features end-to-end – from clarification and experimentation to deployment and initial support
• Translate requirements into user stories and provide implementation plans, as well as own features end-to-end throughout the software development lifecycle - from clarification and experimentation to deployment and initial support
• Identify risks, dependencies, and data limitations early and propose workable solutions
• Challenge unclear requirements and contribute with pragmatic, value-driven alternatives
• Stay close to new developments in LLMs, RAG, prompt optimization, model evaluation, fine-tuning, and applied AI frameworks, and assess where they create real business impact
To succeed and thrive in the role, you bring:
• A degree in Computer Science, Software Engineering, AI, Machine Learning, or similar- or equivalent professional experience
• 3+ years of professional AI engineering/applied data science experience, including hands-on experience with AI, NLP, machine learning, deep learning, or language-model-based applications
• Strong Python skills and experience building clean, maintainable, and production-ready software
• Hands-on experience with GenAI or LLM-based solutions or open-source models
• Solid understanding of software engineering practices (testing, CI/CD, version control, etc.)
• Experience with model evaluation, monitoring, or experiment tracking tools (i.e., MLflow or similar).
• Ability to work in cross-functional, agile teams and communicate clearly in English
Nice to have:
• Experience with cloud platforms such as Google Cloud or similar
• Familiarity with RAG architectures, embeddings, vector databases, and retrieval techniques
• Exposure to fine-tuning or model optimization approaches
• Experience with Kedro for building modular, reproducible data and ML pipelines, and KServe for scalable, production-grade model deployment and inference on Kubernetes
• Knowledge of agentic workflows, tool-calling systems, agentic search, MCP, or A2A integration patterns
What we offer :• Employment based on an employment contract, along with a comprehensive benefits package
• Training and development programs, as well as access to an e-learning platform
• Onboarding program with the support of a dedicated Buddy
• Participation in an annual, company-wide integration event
• A work environment based on Scandinavian organizational culture
• Opportunities for growth through our internal program
Benefits:
• Sharing the costs of sports activities
• Private medical care
• Sharing the costs of foreign language classes
• Sharing the costs of professional training & courses
• Life insurance
• Integration events
• Corporate gym
• Corporate sports team
• Coffee / tea
• Parking space for employees
• Extra social benefits
• Holiday funds
• Christmas gifts
• Employee referral program
• Charity initiatives
• Bicycle parking
• Modern and ergonomic office
• Yoga in the office
• Independently deliver scoped AI, GenAI, NLP, and RAG features of moderate complexity
• Develop and enhance RAG pipelines, including document parsing and ingestion, chunking and metadata strategies, query transformation, retrieval and ranking, response generation and grounding
• Build GenAI features using LLM APIs, structured prompting, and orchestration frameworks (e.g., LangChain, LangGraph, DSPy, etc.)
• Evaluate AI system performance using practical methods such as retrieval metrics, response quality assessment, hallucination analysis, latency measurement, cost analysis, and failure-case testing
• Understand engineering trade-offs across model quality, latency, cost, reliability, maintainability, and implementation complexity
• Own features end-to-end – from clarification and experimentation to deployment and initial support
• Translate requirements into user stories and provide implementation plans, as well as own features end-to-end throughout the software development lifecycle - from clarification and experimentation to deployment and initial support
• Identify risks, dependencies, and data limitations early and propose workable solutions
• Challenge unclear requirements and contribute with pragmatic, value-driven alternatives
• Stay close to new developments in LLMs, RAG, prompt optimization, model evaluation, fine-tuning, and applied AI frameworks, and assess where they create real business impact
To succeed and thrive in the role, you bring:
• A degree in Computer Science, Software Engineering, AI, Machine Learning, or similar- or equivalent professional experience
• 3+ years of professional AI engineering/applied data science experience, including hands-on experience with AI, NLP, machine learning, deep learning, or language-model-based applications
• Strong Python skills and experience building clean, maintainable, and production-ready software
• Hands-on experience with GenAI or LLM-based solutions or open-source models
• Solid understanding of software engineering practices (testing, CI/CD, version control, etc.)
• Experience with model evaluation, monitoring, or experiment tracking tools (i.e., MLflow or similar).
• Ability to work in cross-functional, agile teams and communicate clearly in English
Nice to have:
• Experience with cloud platforms such as Google Cloud or similar
• Familiarity with RAG architectures, embeddings, vector databases, and retrieval techniques
• Exposure to fine-tuning or model optimization approaches
• Experience with Kedro for building modular, reproducible data and ML pipelines, and KServe for scalable, production-grade model deployment and inference on Kubernetes
• Knowledge of agentic workflows, tool-calling systems, agentic search, MCP, or A2A integration patterns
What we offer :• Employment based on an employment contract, along with a comprehensive benefits package
• Training and development programs, as well as access to an e-learning platform
• Onboarding program with the support of a dedicated Buddy
• Participation in an annual, company-wide integration event
• A work environment based on Scandinavian organizational culture
• Opportunities for growth through our internal program
Benefits:
• Sharing the costs of sports activities
• Private medical care
• Sharing the costs of foreign language classes
• Sharing the costs of professional training & courses
• Life insurance
• Integration events
• Corporate gym
• Corporate sports team
• Coffee / tea
• Parking space for employees
• Extra social benefits
• Holiday funds
• Christmas gifts
• Employee referral program
• Charity initiatives
• Bicycle parking
• Modern and ergonomic office
• Yoga in the office
🔍 Dekoder Ogłoszenia
🔴
Independently deliver scoped AI, GenAI, NLP, and RAG features of moderate complexity
Oczekuje się, że będziesz samodzielnie realizować zadania o średnim stopniu trudności, bez ciągłego nadzoru, co może oznaczać dużą odpowiedzialność i potencjalnie brak wsparcia w trudnych momentach.
🔴
Own features end-to-end – from clarification and experimentation to deployment and initial support
Będziesz odpowiedzialny za cały cykl życia funkcji, od pomysłu po wsparcie po wdrożeniu, co może oznaczać długie godziny i szeroki zakres obowiązków.
🔴
Challenge unclear requirements and contribute with pragmatic, value-driven alternatives
Chociaż brzmi to jak zachęta do proaktywności, może też oznaczać, że wymagania często będą niejasne, a od Ciebie oczekuje się nie tylko ich doprecyzowania, ale też proponowania rozwiązań.
🔴
Stay close to new developments in LLMs, RAG, prompt optimization, model evaluation, fine-tuning, and applied AI frameworks, and assess where they create real business impact
Oczekuje się ciągłego uczenia się i śledzenia nowinek technologicznych, co może oznaczać konieczność poświęcania czasu prywatnego na rozwój zawodowy.
🟡
Understand engineering trade-offs across model quality, latency, cost, reliability, maintainability, and implementation complexity
Musisz być świadomy kompromisów między różnymi aspektami technicznymi, co sugeruje, że nie zawsze będzie można osiągnąć idealne rozwiązanie i trzeba będzie podejmować trudne decyzje.