Senior GenAI Engineer

Responsibilities

• Design, develop and maintain scalable backend services and APIs using Python and FastAPI.
• Build and integrate LLM-powered features using OpenAI GPT and Anthropic Claude models.
• Develop and maintain MCP (Model Context Protocol) servers using FastMCP to expose enterprise tools and data to AI agents.
• Create and extend Skills and Plugins that enhance LLM capabilities for business-specific workflows.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking, embedding and retrieval strategies.
• Build agentic solutions using LLM orchestration frameworks such as LangChain, LangGraph or similar.
• Work with vector databases to support semantic search and knowledge retrieval.
• Implement observability and diagnostics for LLM applications: tracing, logging, evaluation, token and cost tracking, latency and quality monitoring.
• Own the full application lifecycle: development, testing, deployment and production support.
• Use AI-assisted development tools (Claude, Codex) to accelerate delivery while maintaining code quality.
• Collaborate with client stakeholders, architects and business analysts to translate requirements into working solutions.
• Ensure solutions meet enterprise standards for security, data privacy and responsible AI use.

Skills Must have

• 4+ years of professional software development experience with a strong focus on Python.
• Solid experience building production REST APIs with FastAPI (or a comparable framework).
• Hands-on experience integrating LLMs (OpenAI GPT, Anthropic Claude) into real applications, including prompt engineering, tool/function calling and structured outputs.
• Practical experience with MCP servers (FastMCP preferred) and LLM tool ecosystems (Skills, Plugins).
• Proven experience designing and implementing RAG pipelines.
• Experience building agentic workflows with orchestration frameworks such as LangChain, LangGraph, LlamaIndex or similar.
• Hands-on experience with at least one vector database (e.g., Pinecone, Weaviate, Qdrant, Chroma, pgvector, Azure AI Search).
• Experience with observability and diagnostics for LLM systems (e.g., LangSmith, Langfuse, Arize Phoenix, OpenTelemetry).
• Experience deploying and running applications in production: Docker, CI/CD, and at least one major cloud platform (AWS, Azure or Google Cloud Platform).
• Daily, confident use of modern development tools including VS Code and AI coding assistants such as Claude and Codex.
• Strong understanding of software engineering best practices: clean code, testing, code review, version control (Git).
• Upper-Intermediate (B2) or higher English, with the ability to communicate directly with US-based stakeholders.