Sr. AI Engineer (Agentic AI & AWS Bedrock) Remote/Hybrid with Travel

Sr. AI Engineer (Agentic AI & AWS Bedrock) Remote/Hybrid with Travel

The key is time zone coverage and so we can expand our search across the West Coast - remote/hybrid but necessarily located in the western time zones. You must be comfortable to travel to the client site for workshops/meetings. 

Overall experience: 12-20+ years Cloud Platform: AWS Primary Language: Python

1. AI Engineer (Agentic AI / AWS Bedrock)
   

Experience: 6–8+ Years
Role: AI Engineer / Generative AI Engineer
Location: USA
Specialization: Agentic AI, AWS Bedrock AgentCore, Claude Agent SDK, Langfuse, AI Observability & Evaluation

Job Summary

We are seeking an experienced AI Engineer with strong hands-on expertise in building, deploying, and monitoring production-grade AI agents and Generative AI solutions using AWS technologies.

The ideal candidate will have experience developing AI agents using Claude Agent SDK, deploying them on Amazon Bedrock AgentCore Runtime, automating deployments through GitHub Actions, and implementing Langfuse for observability and continuous evaluation of agent traffic.

The role requires a strong combination of AI engineering, cloud deployment, data pipeline development, and LLM performance monitoring.

Key Responsibilities

1. Agentic AI Development

• Design, develop, and deploy AI agents using Claude Agent SDK and Python.
  
• Build intelligent, autonomous agents capable of executing complex workflows and interacting with external tools, APIs, and knowledge bases.
  
• Develop agent orchestration workflows with appropriate error handling, retry mechanisms, and guardrails.
  
• Integrate AI agents with enterprise applications and data sources.
  

2. AWS Bedrock & AgentCore

• Deploy and manage AI agents using Amazon Bedrock AgentCore Runtime.
  
• Develop scalable, secure, and production-ready agent architectures on AWS.
  
• Integrate agents with Amazon Bedrock foundation models and enterprise knowledge bases.
  
• Implement agent authentication, access controls, logging, and runtime monitoring.
  

3. Langfuse Deployment & AI Observability

• Deploy, configure, and maintain Langfuse for comprehensive LLM and agent observability.
  
• Integrate Langfuse with AI agents to capture traces, prompts, responses, tool calls, and execution metrics.
  
• Monitor agent performance, response latency, token consumption, and operational costs.
  
• Identify and troubleshoot production issues through distributed tracing and monitoring dashboards.
  

4. Continuous Agent Evaluation

• Design and implement continuous evaluation frameworks for production AI agent traffic.
  
• Evaluate agent responses for accuracy, relevance, hallucinations, task completion, and tool execution quality.
  
• Develop automated evaluation pipelines using Langfuse, LLM-as-a-Judge, and custom evaluation metrics.
  
• Implement continuous feedback mechanisms to improve agent reliability and effectiveness.
  
• Establish quality benchmarks and detect agent performance regressions.
  

5. Data Pipelines & Knowledge Base Integration

• Develop AWS Step Functions workflows integrating Amazon Textract and Amazon Comprehend for intelligent document processing.
  
• Build document ingestion, preprocessing, and data transformation pipelines.
  
• Implement vector database solutions, including document chunking, embedding generation, and vector indexing.
  
• Integrate AI agents with Retrieval-Augmented Generation (RAG) pipelines and enterprise knowledge bases.
  
• Optimize retrieval performance and contextual accuracy.
  

6. CI/CD & Deployment Automation

• Build and maintain CI/CD pipelines using GitHub Actions.
  
• Automate agent code builds, testing, deployment, and configuration management.
  
• Implement deployment workflows for Amazon Bedrock AgentCore Runtime.
  
• Integrate automated agent evaluations into CI/CD processes.
  
• Apply DevOps best practices for version control, release management, and production monitoring.
  

Required Technical Skills

• Programming: Strong Python development experience.
  
• Agentic AI: Claude Agent SDK, AI agent development, agent orchestration, tool calling.
  
• AWS AI Services: Amazon Bedrock, Bedrock AgentCore Runtime, Step Functions, Textract, Comprehend.
  
• Observability: Langfuse deployment, LLM tracing, monitoring, performance analysis.
  
• Evaluation: Continuous agent evaluation, LLM-as-a-Judge, evaluation metrics and automated testing.
  
• DevOps: GitHub Actions, CI/CD pipelines, Git, deployment automation.
  
• RAG: Vector databases, embeddings, chunking strategies, indexing, knowledge base integrations.
  
• Cloud: AWS architecture, IAM, CloudWatch, security, and deployment practices.
  

Good-to-Have Skills

• Experience with LangChain, LangGraph, or other agent orchestration frameworks.
  
• Experience with RAGAS, DeepEval, or similar LLM evaluation frameworks.
  
• Familiarity with Amazon OpenSearch, Pinecone, FAISS, or pgvector.
  
• Experience with Docker, Terraform, or infrastructure as code.
  
• Knowledge of Model Context Protocol (MCP).
  
• Experience with prompt engineering, guardrails, and AI governance.
  
• Experience deploying enterprise Generative AI solutions in production environments.
  

Qualifications

• 10-15+ years of software engineering, cloud engineering, or AI/ML development experience.
  
• Strong hands-on experience building Generative AI or Agentic AI applications.
  
• Demonstrated experience deploying and supporting AI agents in production.
  
• Strong understanding of AWS cloud services, LLM architectures, and automated deployment processes.
  
• Ability to independently design, implement, troubleshoot, and optimize AI agent solutions.
  

Ideal Candidate Profile

A hands-on AI Engineer who can take an agent from development through production deployment and ongoing evaluation. The candidate should be proficient in Claude Agent SDK, AWS Bedrock AgentCore, Langfuse, GitHub Actions, and continuous AI agent evaluation, with the ability to build reliable, scalable, and observable enterprise AI solutions.