AI Developer (Python, RAG, GenAI)

Quick Facts

• Role: AI Developer (Python, RAG, GenAI)

• Focus: Generative AI / LLM solutions using Retrieval-Augmented Generation (RAG)

Description

You will design, build, and scale Generative AI and Large Language Model (LLM) solutions. The role focuses on RAG architectures, integrating LLMs with corporate knowledge sources, and building scalable Python backend services. You’ll optimize prompts and retrieval strategies and implement evaluation and monitoring to improve answer quality and reduce hallucinations.

Responsibilities

• Design and develop RAG-based AI solutions using LLMs and corporate knowledge sources

• Build document pipelines: ingestion, parsing, chunking, embeddings, indexing, retrieval, re-ranking, and answer generation

• Integrate LLMs with vector databases, search services, APIs, and enterprise systems

• Optimize prompts, search strategies, and context management

• Implement evaluation and monitoring for retrieval quality, answer relevance, source compliance, latency, and costs

• Develop scalable backend services and APIs in Python

• Add security, authorization, citation, and information traceability mechanisms

• Collaborate with architecture, engineering, DevOps/MLOps, and business teams for testing, deployment, and continuous improvement

Requirements

• Strong Python skills and experience building production backend applications and AI services

• Hands-on experience with Generative AI and LLMs (prompt engineering, context management, structured outputs)

• Experience designing and implementing RAG solutions

• Strong end-to-end RAG knowledge (document processing, chunking, embeddings, indexing, retrieval, re-ranking, generation)

• Experience with at least one LLM platform/API (Azure OpenAI, OpenAI API, AWS Bedrock, Google Vertex AI)

• Experience with AI orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or similar)

• Experience with vector databases and semantic search (Azure AI Search, PostgreSQL/pgvector, Pinecone, Weaviate, Qdrant, Elasticsearch, OpenSearch)

• Knowledge of Git, automated tests, and modern software engineering practices

• Practical Docker and containerization experience

• Experience with CI/CD pipelines and deploying to cloud environments

• English proficiency B2/C1 (required)

Benefits

No benefits were provided in the source text.