AI Architect
DEVAPO SPÓŁKA Z OGRANICZONĄ ODPOWIEDZIALNOŚCIĄ
⚲ Warszawa, Ochota
Do uzgodnienia
Wymagania
- Python
- LangChain
- LangGraph
- GCP Vertex
- Docker
- Qdrant
- Weaviate
- Pinecone
- pgvector
- Databricks
- Azure AI
- Foundry
- AWS Bedrock
Opis stanowiska
Nasze wymagania:
Proven experience building LLM-based systems that run in production — POC to production, not just experimentation
Solid Python skills and hands-on experience with the AI stack: LangChain, LangGraph, vector databases (Qdrant, Weaviate, Pinecone, pgvector or similar)
Working knowledge of RAG — you understand why naive chunking fails and what to do about it
Experience designing AI system architecture: integration patterns, scalability, security, cost trade-offs
Ability to translate business requirements into technical designs and communicate them clearly to both engineers and stakeholders
Familiarity with cloud AI services: Azure AI / AWS Bedrock / GCP Vertex
You know how to ship: REST APIs, Docker, cloud basics
Awareness of AI governance, responsible AI practices, and regulatory context (EU AI Act, GDPR, DORA)
English B2+ — client-facing role, calls and written communication included
Mile widziane:
Experience evaluating LLM outputs (RAGAS, LangSmith, Arize or similar)
MLflow or another experiment tracking tool
Databricks, Azure AI Foundry or AWS Bedrock
Fine-tuning experience (LoRA, PEFT, anything hands-on)
Kafka or streaming pipelines for real-time AI use cases
O projekcie:
We are looking for an AI Architect who genuinely gets excited about building things with AI — someone who shipped something real with LLMs, not just played with the API. You will work on international projects for clients in banking, insurance, and telco (US, Netherlands, UK), translating messy business problems into working AI systems.
Zakres obowiązków:
Designing GenAI and LLM-based architectures for enterprise use cases — translating business requirements into concrete technical designs
Building and delivering POCs and MVPs quickly, then evolving them into production-grade systems
Designing RAG pipelines: chunking strategies, embedding, vector indexing, retrieval quality
Implementing and reviewing agentic workflows (LangChain, LangGraph, CrewAI or similar)
Integrating LLMs (OpenAI, Anthropic, Gemini, open-source) into existing enterprise stacks — ERP, CRM, BPM, data platforms
Creating reusable architectural patterns and engineering standards that scale across projects
Working directly with clients: understanding requirements, presenting solutions, supporting presales and RFP responses
Writing Architecture Decision Records and technical documentation for both technical and business audiences
Mentoring engineering teams
Oferujemy:
Certifications and training funded
Private medical care (Medicover)
Multisport card
English language classes
Flexible working hours
Team meetups and integration events
Referral bonus
Proven experience building LLM-based systems that run in production — POC to production, not just experimentation
Solid Python skills and hands-on experience with the AI stack: LangChain, LangGraph, vector databases (Qdrant, Weaviate, Pinecone, pgvector or similar)
Working knowledge of RAG — you understand why naive chunking fails and what to do about it
Experience designing AI system architecture: integration patterns, scalability, security, cost trade-offs
Ability to translate business requirements into technical designs and communicate them clearly to both engineers and stakeholders
Familiarity with cloud AI services: Azure AI / AWS Bedrock / GCP Vertex
You know how to ship: REST APIs, Docker, cloud basics
Awareness of AI governance, responsible AI practices, and regulatory context (EU AI Act, GDPR, DORA)
English B2+ — client-facing role, calls and written communication included
Mile widziane:
Experience evaluating LLM outputs (RAGAS, LangSmith, Arize or similar)
MLflow or another experiment tracking tool
Databricks, Azure AI Foundry or AWS Bedrock
Fine-tuning experience (LoRA, PEFT, anything hands-on)
Kafka or streaming pipelines for real-time AI use cases
O projekcie:
We are looking for an AI Architect who genuinely gets excited about building things with AI — someone who shipped something real with LLMs, not just played with the API. You will work on international projects for clients in banking, insurance, and telco (US, Netherlands, UK), translating messy business problems into working AI systems.
Zakres obowiązków:
Designing GenAI and LLM-based architectures for enterprise use cases — translating business requirements into concrete technical designs
Building and delivering POCs and MVPs quickly, then evolving them into production-grade systems
Designing RAG pipelines: chunking strategies, embedding, vector indexing, retrieval quality
Implementing and reviewing agentic workflows (LangChain, LangGraph, CrewAI or similar)
Integrating LLMs (OpenAI, Anthropic, Gemini, open-source) into existing enterprise stacks — ERP, CRM, BPM, data platforms
Creating reusable architectural patterns and engineering standards that scale across projects
Working directly with clients: understanding requirements, presenting solutions, supporting presales and RFP responses
Writing Architecture Decision Records and technical documentation for both technical and business audiences
Mentoring engineering teams
Oferujemy:
Certifications and training funded
Private medical care (Medicover)
Multisport card
English language classes
Flexible working hours
Team meetups and integration events
Referral bonus
🔍 Dekoder Ogłoszenia
🟡
Proven experience building LLM-based systems that run in production — POC to production, not just experimentation
Oczekuje się, że kandydat ma udokumentowane sukcesy w wdrażaniu systemów opartych na LLM, które działają w rzeczywistych środowiskach produkcyjnych, a nie tylko w fazie prototypowania.
🟡
Ability to translate business requirements into technical designs and communicate them clearly to both engineers and stakeholders
Oczekuje się, że kandydat potrafi zrozumieć potrzeby biznesowe i przełożyć je na konkretne rozwiązania techniczne, a także efektywnie komunikować się z różnymi grupami odbiorców.
🟡
You know how to ship: REST APIs, Docker, cloud basics
Wymaga się praktycznej znajomości tworzenia i wdrażania aplikacji, w tym budowania API, konteneryzacji i podstawowych usług chmurowych.
🟡
Awareness of AI governance, responsible AI practices, and regulatory context (EU AI Act, GDPR, DORA)
Oczekuje się, że kandydat rozumie i potrafi stosować zasady etyczne, regulacyjne i prawne związane z rozwojem i wdrażaniem sztucznej inteligencji.
🟡
We are looking for an AI Architect who genuinely gets excited about building things with AI — someone who shipped something real with LLMs, not just played with the API.
Firma szuka osoby z pasją do tworzenia rzeczywistych rozwiązań AI, która ma udokumentowane doświadczenie w praktycznym wdrażaniu modeli językowych, a nie tylko teoretyczną wiedzę.