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Known employer and mid-level seniority increase applicants, but niche LLM+AWS skills moderate competition.
High due to specialized LLM, AWS, and production ML engineering requirements limiting transferability.
High due to explicit 6+ years, mandatory AWS/LLM production experience, and specific IaC/CI-CD tooling.
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Design, develop, and operate scalable production AI/ML services and applications on AWS, focusing on Generative AI/LLM and backend APIs.
Implement and maintain CI/CD pipelines, containerized deployments, and infrastructure-as-code for AI/data services.
Integrate AI agents, tool ecosystems, retrieval-augmented generation (RAG), and evaluation pipelines to enhance AI application quality.
6+ years of professional experience in software, data, or AI/ML engineering.
3+ years hands-on experience building and operating production services on AWS with services including Lambda, ECR, S3, API Gateway, IAM, CloudWatch.
Strong proficiency in Python with solid software engineering practices (testing, code reviews, version control).
Hands-on experience with CI/CD tools (GitHub Actions, Jenkins, or similar), containerization (Docker), and Infrastructure-as-Code (AWS CDK, Terraform, or CloudFormation).
Strong hands-on engineer with practical implementation experience in Python, AWS, Generative AI/LLM applications, APIs, and cloud-native DevOps.
Experience or familiarity with Model Context Protocol (MCP), Bedrock AgentCore, RAG, knowledge graphs, and AI evaluation pipelines is a strong advantage.
Capable of working independently within existing architectures and codebases, delivering solutions with minimal supervision.