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Metro locations, mid-level 3-6 years, popular ML title, and broad GenAI/AWS requirements increase applicant competition.
GenAI and AWS Bedrock specialization narrows industry fit despite transferable core ML engineering skills.
Explicit 3-6 years plus many mandatory GenAI, LangChain, and AWS Bedrock technical requirements raise strictness.
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Develop and deploy GenAI applications leveraging AWS Bedrock and foundation models like Claude Haiku and Claude Sonnet.
Architect and implement ML pipelines using technologies such as Titan Embeddings and Amazon OpenSearch Vector Search for vector-based retrieval.
Create AI agents and knowledge base solutions for customer query automation including document parsing, chunking, vectorizing, and re-ranking strategies with security guardrails on AWS Bedrock.
3-6 years of relevant experience in machine learning and AI application development.
Proficiency in Python (including Pandas, NumPy, FastAPI).
Hands-on experience with AWS Bedrock, foundation models, and related services such as API Gateway, Lambda, S3, IAM, CloudWatch, ECR, and SageMaker.
Experience with Agentic AI frameworks (e.g., LangChain, LangGraph, LlamaIndex, CrewAI) and building RAG pipelines for LLM & GenAI applications.
Experienced in designing secure, scalable AI workflows with Agentic AI frameworks and AWS Bedrock AgentCore for enterprise applications.
Skilled in software engineering best practices including Git, REST APIs, CI/CD pipelines, and Agile/DevOps environments.
Familiar with advanced ML operations (MLOps), containerization (Docker, Kubernetes), and database technologies (Redshift, SQL, DynamoDB) to support GenAI implementations.