About the Role
About the position
Design, build, and operate production-grade agentic AI systems on AWS and Azure. The Senior AI Developer will turn business problems into reliable, multi-step agent workflows using LLMs, tools, and enterprise data, and will own solutions from architecture and prototyping through deployment, evaluation, and monitoring. The role also sets engineering standards for building with AI.\n\nThe position involves developing agentic workflows and LLM applications, building retrieval-augmented generation pipelines, integrating agents with enterprise systems, and implementing evaluation, safety, and responsible-AI controls. It also includes deploying and monitoring services, optimizing performance and cost, mentoring engineers, and collaborating with product, data, security, and architecture teams to bring AI use cases into production.
Responsibilities
Design and build single- and multi-agent AI workflows with planning, tool use, memory, and human-in-the-loop controls.Develop LLM applications using AWS and Azure AI services, and recommend an appropriate platform and model for each use case.Build RAG pipelines involving data ingestion, chunking, embeddings, vector search, and grounding over enterprise data.Integrate agents with internal systems, APIs, and SaaS tools using standards such as MCP and function or tool calling.Implement evaluation frameworks for quality, accuracy, safety, and cost, including regression tests, LLM-as-judge methods, and human review.Implement guardrails and responsible-AI controls, including prompt-injection defenses, content filtering, PII handling, access control, and audit logging.Containerize and deploy services using CI/CD and infrastructure as code, and add observability for tracing, logging, latency, and token costs.Optimize performance, reliability, and cost across models, prompts, and infrastructure.Mentor engineers, conduct design and code reviews, and document architectures and decisions.Collaborate with product owners, data teams, security, and architecture to take AI use cases from idea to production.Requirements
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent experience.At least 5 years of software engineering experience, including at least 2 years building LLM or GenAI applications in production.Strong Python skills and experience building and operating APIs and services.Hands-on experience building agentic systems with a modern framework such as LangGraph, LangChain, Microsoft Agent Framework / Semantic Kernel, AutoGen, Strands Agents, or CrewAI.Production experience with AI services on at least one of AWS or Azure, and working knowledge of the other.Practical RAG experience with vector databases or search services such as OpenSearch, Azure AI Search, pgvector, or Pinecone.Cloud fundamentals experience, including containers, networking, identity (IAM / Entra ID), and secrets management.Experience with CI/CD, Git, and infrastructure as code such as Terraform, Bicep, or CloudFormation.Understanding of LLM evaluation, prompt engineering, and safety and security risks specific to AI systems.Strong communication skills and ability to explain trade-offs to technical and non-technical stakeholders.Nice-to-haves
Experience with MCP servers, multi-agent protocols such as A2A, or agent orchestration at scale.Fine-tuning or model customization experience, and experience with open-source models.Background in a regulated sector such as banking, healthcare, or government.Cloud certifications such as AWS Certified AI Practitioner or Solutions Architect, Azure AI-102, AZ-204, or AZ-400.Experience with data engineering tools such as Spark, Databricks, Fabric, or Glue, and data governance.Experience mentoring or leading small teams.