Senior Python AI/ML Developer
Responsibilities
- Ability to collaborate closely with machine learning engineers and data scientists to refine AI models, develop robust model embeddings, and optimize prompts for accuracy, performance, and cost-efficiency
- Develop and optimize AI agent systems, including multi-agent orchestration and tool integration
- Implement and iterate on RAG pipelines, evaluation frameworks, and LLM-powered data processing solutions
- Maintain and enhance applications and solutions on Python
Requirements
- A solid knowledge of Python, with experience in writing clean, efficient, and maintainable code
- Ability to design and develop autonomous agents using frameworks such as LangGraph/CrewAI/OpenAI Agents SDK, enabling intelligent decision-making and task automation
- Experience in rapid prototyping using large language models (LLMs), vector databases, and knowledge graphs to explore and implement new AI-driven capabilities
- Expertise in retrieval-augmented generation (RAG) workflows and document retrieval techniques to enhance content relevance
- Hands-on experience working with large-scale language models, such as GPT/Claude/Gemini/Qwen, along with a solid understanding of prompt engineering, embedding techniques
- Familiarity with the Model Context Protocol (MCP) for connecting LLM agents to external tools and data sources
- Experience in rapid prototyping of AI-driven capabilities using LLMs, embeddings, and structured/unstructured data sources
- Experience with code generation and AI-assisted development tools (Claude Code, GitHub Copilot, Cursor, Codex)
- Strong written and spoken English communication skills (B2)
Nice to have
- Experience with Python frameworks such as Django, Flask, or FastAPI
- Proficiency in SQL for working with structured databases, querying data, and optimizing database performance
- Familiarity with platforms Dataiku, Snowflake, Tableau, Streamlit, Artifactory, dbt
- Hands-on experience with NLP deep learning frameworks such as PyTorch, TensorFlow, and Transformers
- Familiarity with graph RAG approaches (Microsoft GraphRAG, LightRAG, or Neo4j-based solutions)
- Understanding AI cost optimization: model routing, caching strategies (semantic caching), prompt compression, and token management
We offer
- Competitive compensation
- Remote or office work
- Flexible working hours
- A team with excellent tech expertise
