We’re seeking a Senior AI Engineer to lead the design and implementation of LLM pipelines, RAG systems, and multi-agent frameworks, powering the next generation of AI solutions for enterprise clients.

EXPERIENCE

• 8+ years in software engineering, with 3+ years in AI/ML, NLP, or LLMs
• Expertise in Python — building, debugging, and scaling modular, production-grade systems
• Deep hands-on experience with LangChain, LangGraph, CrewAI, AutoGen, Hugging Face
• Strong knowledge of semantic search, embeddings, vector databases (Azure AI Search, FAISS, Postgres/pgvector)
• Experience with CI/CD pipelines, Docker, GitHub, RESTful APIs
• Skilled in RAG pipeline performance evaluation (semantic similarity, faithfulness, etc.)
• Familiarity with observability, telemetry, and AI-powered development tools like Copilot, Cursor
• Strong communication skills — able to explain complex AI models to technical and executive audiences

QUALIFICATIONS

• Experience with multimodal LLMs and vision-language models
• Knowledge of ReAct-style prompting, planner-executor agents
• Experience in agile methodologies and cross-functional teams
• Track record of evaluating RAG pipelines using both structured and human-in-the-loop methods

ABOUT US

We are a software house with 18 years of experience and a global portfolio of projects. We help businesses modernize, scale, and innovate through custom software solutions - always with a focus on flexibility and quality. Our team embraces unconventional ideas and new technologies, delivering solutions that drive real impact. If you value professionalism, creativity, and a strong engineering culture, you’ll feel at home here.
Job Type: Full-time
Pay: €54,000.00-€120,000.00 per year

Experience:

• Software Engineering: 8 years (required)
• AI: 3 years (required)

Work Location: Remot

• Architect end-to-end LLM pipelines using RAG, embeddings, and orchestration layers
• Design modular, scalable multi-agent systems with LangChain, LangGraph, CrewAI, or AutoGen
• Evaluate model options (e.g., OpenAI, Azure OpenAI) for performance, cost, and capabilities
• Build ingestion pipelines for unstructured data (PDFs, JSON, HTML) using LangChain, pyPDF
• Deploy and integrate vector stores like Azure AI Search, FAISS, ChromaDB
• Develop APIs, CI/CD pipelines, Dockerized environments, and reusable Python libraries
• Optimize distributed Python systems across multiple services and APIs
• Implement telemetry and observability via Datadog, LangFuse, or custom solutions
• Define metrics (faithfulness, precision, recall, semantic similarity) using sklearn, NumPy, RAGAS
• Lead design sessions, mentor developers, and communicate AI insights to stakeholders
• Build interactive prototypes with Streamlit, LangFlow, or Jupyter Notebooks