Hiring AI Engineer Generative AI Engineer | (Agentic AI,AWS Bedrock AgentCore,Claude Agent SDK,Langfuse) | Los Angeles, CA (Hybrid)

Role: AI Engineer / Generative AI Engineer

Duration: 3 Months (Extension Possible)

Location: Los Angeles CA (Hybrid)

Specialisation: 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

• 68 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.

Additional Information :

All your information will be kept confidential according to EEO guidelines.

Remote Work :

No

Employment Type :

Contract