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We're looking for a Senior AI Developer to design, build and run production-grade agentic AI systems on AWS and Azure. You'll turn business problems into reliable multi-step agent workflows that use LLMs, tools and enterprise data. You'll own solutions from architecture and prototyping through deployment, evaluation and monitoring, and you'll set engineering standards for how we build with AI. What you will do Design and build agentic AI systems: single and multi-agent workflows with planning, tool use, memory and human-in-the-loop controls. Develop LLM applications on AWS (Amazon Bedrock, Bedrock Agents/AgentCore) and Azure (Azure OpenAI, Microsoft Foundry / Azure AI Foundry), and recommend the right platform and model for each use case. Build retrieval-augmented generation (RAG) pipelines, including data ingestion, chunking, embeddings, vector search and grounding over enterprise data. Integrate agents with internal systems, APIs and SaaS tools, using standards such as the Model Context Protocol (MCP) and function/tool calling. Implement evaluation frameworks for quality, accuracy, safety and cost, including regression tests, LLM-as-judge methods and human review. Add guardrails and responsible-AI controls: prompt-injection defences, content filtering, PII handling, access control and audit logging. Containerise and deploy services with CI/CD and infrastructure as code, and add observability (tracing, logging, latency and token-cost monitoring). Optimise performance, reliability and cost across models, prompts and infrastructure. Mentor engineers, run design and code reviews, and document architectures and decisions. Work with product owners, data teams, security and architecture to take AI use cases from idea to production. Requirements What you need to be successful Must have Bachelor's degree in Computer Science, Engineering or a related field (or equivalent experience). 5+ years of software engineering experience, with 2+ years building LLM or GenAI applications in production. Strong Python skills, plus experience building and operating APIs and services. Hands-on experience building agentic systems with a modern framework (for example LangGraph, LangChain, Microsoft Agent Framework / Semantic Kernel, AutoGen, Strands Agents or CrewAI). Production experience on at least one of AWS or Azure AI services, and working knowledge of the other. Practical RAG experience with vector databases or search services (for example OpenSearch, Azure AI Search, pgvector or Pinecone). Experience with cloud fundamentals: containers (Docker, Kubernetes or serverless), networking and identity (IAM / Entra ID), and secrets management. Experience with CI/CD, Git and infrastructure as code (Terraform, Bicep or CloudFormation). Understanding of LLM evaluation, prompt engineering, and safety and security risks specific to AI systems. Strong communication skills and the ability to explain trade-offs to technical and non-technical stakeholders. Nice to have: Experience with MCP servers, multi-agent protocols (for example A2A), or agent orchestration at scale. Fine-tuning or model customization, and experience with open-source models. Background in a regulated sector such as banking, healthcare or government. Cloud certifications (for example AWS Certified AI Practitioner or Solutions Architect, Azure AI-102 or AZ-204, AZ-400). Experience with data engineering (Spark, Databricks, Fabric or Glue) and data governance. Experience mentoring or leading small teams. Originally posted on Himalayas