GENERAL FUNCTION:
We are hiring a Sr AI AWS Engineer who has actually built AI/ML applications in cloud-not just read about them. This role centers on hands-on development of retrieval-augmented generation (RAG) systems, fine-tuning LLMs, and AWS-native microservices that drive automation, insight, and governance in an enterprise environment. You'll design and deliver scalable, secure services that bring large language models into real operational use-connecting them to live infrastructure data, internal documentation, and system telemetry.
You'll be part of a high-impact team pushing the boundaries of cloud-native AI in a real-world enterprise setting. This is not a prompt-engineering sandbox or a resume keyword trap. If you've merely dabbled in BedRock, mentioned RAG on LinkedIn, or read about vector search-this isn't the right fit. We're looking for candidates who have architected, developed, and supported AI/ML services in production environments.
This is a builder's role within our Public Cloud AWS Engineering team. We aren't hiring buzzword lists or conference attendees. If you've built something you're proud of-especially if it involved real infrastructure, real data, and real users-we'd love to talk. If you're still learning, that's great too-but this isn't an entry-level role or a theory-only position.
DUTIES AND RESPONSIBILITIES:
- Hands-on role using AWS (Lambda, Bedrock, SageMaker, Step Functions, DynamoDB, S3).
- Responsible for the implementation of AWS cloud services including infrastructure, machine learning, and artificial intelligence platform services.
- Experience with LLM-based applications, including Retrieval-Augmented Generation (RAG) using LangChain and other frameworks.
- Develop cloud-native microservices, APIs, and serverless functions to support intelligent automation and real-time data processing.
- Collaborate with internal stakeholders to understand business goals and translate them into secure, scalable AI systems.
- Own the software release lifecycle, including CI/CD pipelines, GitHub-based SDLC, and infrastructure as code (Terraform).
- Support the development and evolution of reusable platform components for AI/ML operations.
- Create and maintain technical documentation for the team to reference and share with our internal customers.
- Excellent verbal and written communication skills in English.
SUPERVISORY RESPONSIBILITIES: None
MINIMUM KNOWLEDGE, SKILLS, AND ABILITIES REQUIRED:
- 7 years of hands-on software engineering experience with a strong focus on Python.
- Experienced with AWS services, especially Bedrock or SageMaker
- Familiar with fine-tuning large language models or building datasets and/or deploying ML models to production.
- Demonstrated experience with AWS organizations and policy guardrails (SCP, AWS Config).
- Solid experience implementing RAG architectures and LangChain.
- Demonstrated experience in Infrastructure as Code best practices and experience with building Terraform modules for AWS cloud.
- Strong background in Git-based version control, code reviews, and DevOps workflows.
- Demonstrated success delivering production-ready software with release pipeline integration.
Nice-to-Haves:
- AWS or relevant cloud certifications.
- Policy as Code development (e.g., Terraform Sentinel).
- Experience with Hugging Face, Golang, or Node.js.
- Exposure to FinOps and cloud cost optimization.
- Data science background or experience working with structured/unstructured data.
- Awareness of data privacy and compliance best practices (e.g., PII handling, secure model deployment).