AWS Agentic AI Engineer (Hybrid)

Job description:

Experienced AWS Agentic AI Engineer to design, build, and deploy autonomous and semi-autonomous AI agents on AWS. This role focuses on developing goal-driven, tool-using, multi-step AI systems capable of reasoning, planning, executing actions, and collaborating with humans and enterprise systems to solve real-world business problems.

The position sits at the intersection of Large Language Models (LLMs), cloud-native architecture, APIs, and automation, delivering production-grade agentic AI solutions using AWS AI/ML and cloud services.

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.