Gen AI Developer (Contract)

Role overview

Core responsibilities

• Develop and optimize Python backend APIs and microservices.
• Build RAG pipelines covering document ingestion, preprocessing, chunking, embeddings, retrieval, and indexing.
• Work with vector databases for semantic search and context retrieval.
• Develop LLM applications using LangChain, LangGraph, and LangFuse.
• Deploy LLM solutions with Amazon Bedrock and integrate foundation models into enterprise applications.
• Design AI agents with tool calling, workflow orchestration, memory, and multi-step reasoning.
• Develop system prompts and context-engineering strategies to improve application quality.
• Implement SSO-based authentication and authorization.
• Containerize applications with Docker and support scalable deployment.
• Implement monitoring for APIs, LLM applications, model performance, latency, failures, and usage.
• Optimize performance, scalability, reliability, security, and cost efficiency.
• Troubleshoot production issues and continuously improve deployed GenAI applications.

Required experience and knowledge

• Experience with scalable GenAI applications using LLMs, RAG, and agent-based architectures.
• Python development experience with backend APIs and microservices.
• Experience building end-to-end RAG pipelines and working with vector databases.
• Experience with LangChain, LangGraph, LangFuse, and Amazon Bedrock.
• Experience implementing SSO authentication and authorization for enterprise applications.
• Experience containerizing services with Docker and supporting scalable enterprise deployment.
• Knowledge of GenAI security, data privacy, prompt management, evaluation, and responsible AI.

Workplace details

• No explicit remote, hybrid, onsite, or field-work arrangement is provided.