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Ai Engineer Generative Ai Engineer

Posted on: Oct 22, 2026

Nexiva Inc
Los Angeles, California
Onsite
CTC

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Job Title :

Ai Engineer Generative Ai Engineer

Job Type :

Contract

Job Location :

Los Angeles California United States

Remote :

Job Description :

Role: AI Engineer / Generative AI Engineer
Location is Los Angeles, California.
Specialization: Agentic AI, AWS Bedrock AgentCore, Claude Agent SDK, Langfuse, AI Observability & Evaluation

Experience: 6 8+ Years

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 productionlos -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

6 8+ 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.

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Position Details

Posted:

Oct 22, 2026

Reference Number:

1562-46931

Employment:

CTC

City:

Los Angeles

Job Origin:

CIEPAL_ORGANIC_FEED

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