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Mid-level, metro, popular ML role with broad GenAI requirements increases candidate competition.
Role requires specialized GenAI, LLM frameworks and AWS Bedrock, making cross-industry transferability low.
Many mandatory GenAI, AWS Bedrock, and LLM tool requirements plus 4–7 years experience enforce strict filtering.
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Design and develop advanced machine learning models and algorithms for complex business problems.
Optimize and deploy ML models on AWS infrastructure ensuring scalability and reliability.
Develop AI agents and RAG pipelines using Agentic AI frameworks and AWS Bedrock technologies.
4-7 years of relevant experience in machine learning engineering.
Proficient in Python (Pandas, NumPy, FastAPI) and hands-on experience with AWS Bedrock and foundation models such as Claude Haiku and Claude Sonnet.
Experience with LLM & GenAI including RAG pipelines, prompt engineering, and vector-based retrieval techniques.
Hands-on experience with AWS services like API Gateway, Lambda, S3, IAM, CloudWatch, ECR, and SageMaker.
Experienced with Agentic AI frameworks including LangChain, LangGraph, LlamaIndex, or CrewAI and AWS Bedrock AgentCore for AI agents and workflow orchestration.
Familiarity with securing AI applications through AWS Bedrock Guardrails and implementing authentication and authorization.
Exposure to software engineering best practices including Git, REST APIs, CI/CD pipelines, and working in Agile and DevOps environments.