AWS Agentic AI Engineer (Hybrid)

Experience & Qualifications: Bachelor’s degree in computer science, Engineering, Technology, or a related field, with 8–12 years of overall IT experience and at least 3+ years of hands‑on experience building agentic AI solutions using AWS cloud‑native services. Strong expertise in core AWS services including AWS Lambda, Step Functions, EventBridge, S3, DynamoDB or Aurora, OpenSearch, Amazon Bedrock, and Amazon SageMaker. Advanced proficiency in Python for building scalable AI systems and automation workflows. Proven experience leading the technical design and implementation of complex AI agent architectures and components. Hands‑on experience designing Retrieval Augmented Generation (RAG) solutions and applying model fine‑tuning techniques. Demonstrated ability to manage performance, scalability, reliability, and cost optimization of AI workloads in production environments. Proven experience conducting code reviews, leading knowledge‑sharing sessions, and making critical decisions on technologies, architectures, and frameworks. Healthcare domain experience is desirable, along with knowledge of AI safety, governance, compliance, and responsible AI practices. AWS certifications such as Solutions Architect or Machine Learning Specialty are preferred, and experience with LangChain and LangGraph is a plus. Excellent communication skills with a strong ability to collaborate effectively with both technical and non-technical stakeholders.

Roles & Responsibilities: Design and develop agentic AI systems using Amazon Bedrock and other foundation models, including Claude and Titan. Build autonomous and semi‑autonomous AI agents capable of multi‑step reasoning, planning, tool usage, action execution, and human‑in‑the‑loop collaboration. Integrate with Amazon SageMaker for custom model training, fine‑tuning, evaluation, and experimentation. Implement Retrieval Augmented Generation (RAG) solutions using Amazon OpenSearch, Amazon Aurora, DynamoDB, and vector databases such as FAISS or Pinecone. Optimize inference cost, latency, scalability, and reliability across AI workloads. Implement guardrails, validation layers, and human‑in‑the‑loop controls to ensure safe, reliable, and predictable AI behavior. Address hallucination mitigation, prompt injection risks, bias concerns, and model misuse scenarios. Ensure compliance with enterprise AI governance, security, and regulatory standards. Implement comprehensive logging, monitoring, observability, and audit trails for AI systems. Support production deployments, incident resolution, and root cause analysis for AI services. Continuously improve agent performance, reliability, robustness, and usability through iteration, monitoring, and feedback. Collaborate closely with architecture, DevOps, data engineering, security, and business teams to deliver end‑to‑end AI solutions.