Senior AI Engineer
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Summary
Hands-on senior AI engineer building production Generative AI systems: RAG pipelines, LLM agent workflows (LangChain/LangGraph/LlamaIndex), and the Python backend services behind them, deployed on AWS with Kubernetes, Terraform, and CI/CD. A purely technical IC role — write code daily, mentor via code reviews, own end-to-end delivery.
We're looking for a hands-on AI Engineer who combines strong backend engineering fundamentals with hands-on experience building production Generative AI systems. You'll design and ship RAG pipelines, integrate LLMs into real products, and build the backend services that support them, writing code daily, not just architecting on paper.
This is a purely technical IC role, not a managerial one. You’ll lead by example, mentor through code reviews, and own end-to-end technical delivery.
- Build and maintain RAG systems, including retrieval, re-ranking, embeddings, and vector databases.
- Build AI agent workflows using LangChain, LangGraph, LlamaIndex, AutoGen, or similar tools.
- Develop backend services and APIs using Python, including async programming and multithreading.
- Deploy and manage AI applications using AWS, Kubernetes, Terraform, Helm, and CI/CD.
- Build event-driven systems using services such as Lambda, SQS, SNS, S3, and CloudWatch.
- Work with tools such as API Gateway, LiteLLM, AWS Bedrock, and SageMaker.
- Monitor, debug, test, and improve AI systems running in production.
Must-Have
- Around 5+ years of experience, with strong backend development.
- Strong Python and software engineering fundamentals.
- Proven experience building and running production systems, not just PoCs.
- Hands-on experience with RAG, embeddings, retrieval, re-ranking, and vector databases.
- Experience with LangChain, LangGraph, LlamaIndex, AutoGen, or similar frameworks.
- Experience with AWS and cloud-native technologies, including Terraform, Kubernetes, Helm, and CI/CD.
Nice to Have
- Experience with AWS Bedrock or SageMaker.
- Experience with document/OCR pipelines or PyTorch.
- Experience with AI evaluation, hallucination detection, monitoring, or LLMOps.
- Builder mindset: Enjoys writing, debugging, and improving production code.
- Ownership: Takes solutions from development through production.
- Collaboration: Works effectively across backend, data, and platform teams.
- Clear communication: Explains technical decisions clearly.