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Metro location, mid-level ML title, and generalist hiring increase competition despite niche GenAI requirements.
Specialized GenAI, AWS Bedrock, and agentic-AI experience moderately narrows cross-industry portability.
Explicit 3–6 years plus many mandatory GenAI, AWS Bedrock, and agentic AI skills make shortlisting highly selective.
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Develop and implement LLM-based Generative AI applications using AWS Bedrock and Agentic AI frameworks.
Architect and manage ML pipelines involving vector-based retrieval, RAG pipelines, and AI agents for customer query automation and knowledge base solutions.
Ensure security and compliance by configuring AWS Bedrock Guardrails and managing authentication and authorization for AI applications.
3-6 years of relevant experience in machine learning with a focus on generative AI and AWS services.
Hands-on experience with Python (including Pandas, NumPy, FastAPI) and AWS Bedrock including foundation models like Claude Haiku/Sonnet.
Proficiency in developing RAG pipelines, prompt engineering, Agentic AI frameworks (e.g., LangChain, LangGraph, LlamaIndex) and AWS services such as API Gateway, Lambda, S3, IAM, CloudWatch, ECR, SageMaker.
Relevant AWS certifications (AWS Certified AI Practitioner, AWS Certified ML Engineer – Associate, or AWS Certified Solutions Architect – Associate) are a plus.
Experienced in building secure, production-grade GenAI applications with scalable ML pipeline architecture on AWS cloud.
Strong practitioner in Agentic AI frameworks and AI agent development for automating customer service and knowledge management.
Familiar with DevOps practices, CI/CD pipelines, and managing AI workflows in agile environments with a focus on operationalizing LLMs and foundation models.