Senior AI Engineer, Agentic AI, AWS Bedrock

Simple Software Solutions Group, Inc

. Design, develop, and deploy AI agents using Claude Agent SDK and Python .

Posted 10/23/2026contractJacksonville • Florida • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in designing, developing, and deploying AI agents using Claude Agent SDK and Python, with a strong focus on AWS cloud services and automated deployment processes. Proficient in building scalable, production-ready architectures and implementing continuous evaluation frameworks for AI agent performance.

Highest-signal resume keywords

AI Agent DevelopmentAWS Cloud ServicesPython DevelopmentCI/CD PipelinesAgent Orchestration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonAI Agent DevelopmentAWS Step FunctionsLangfuseClaude Agent SDKGenerative AIVector DatabasesAutomated TestingDeployment AutomationMonitoring and Performance Analysis

Soft Skills

Problem-SolvingIndependent Design and ImplementationTroubleshooting

Tools & Technologies

Amazon BedrockGitHub ActionsAmazon TextractAmazon ComprehendDistributed TracingMonitoring DashboardsDockerTerraform

Industry Keywords

Generative AIAI GovernanceEnterprise ApplicationsProduction DeploymentKnowledge Base Integration

Tech Stack

Tools & technologies

AWSCloudDockerPythonTerraform

About the role

Key responsibilities & impact

Design, develop, and deploy AI agents using Claude Agent SDK and Python
Build autonomous agents that execute complex workflows and interact with external tools, APIs, and knowledge bases
Develop agent orchestration workflows with error handling, retry mechanisms, and guardrails
Integrate AI agents with enterprise applications and data sources
Deploy and manage AI agents using Amazon Bedrock AgentCore Runtime
Develop scalable, secure, 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
Deploy, configure, and maintain Langfuse for LLM and agent observability
Capture traces, prompts, responses, tool calls, and execution metrics
Monitor agent performance, response latency, token consumption, and operational costs
Troubleshoot production issues using distributed tracing and monitoring dashboards
Design and implement continuous evaluation frameworks for production AI agent traffic
Evaluate 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 feedback mechanisms to improve agent reliability and effectiveness
Establish quality benchmarks and detect performance regressions
Develop AWS Step Functions workflows integrating Amazon Textract and Amazon Comprehend
Build document ingestion, preprocessing, and data transformation pipelines
Implement vector database solutions, including chunking, embedding generation, and vector indexing
Integrate agents with RAG pipelines and enterprise knowledge bases
Optimize retrieval performance and contextual accuracy
Build and maintain CI/CD pipelines using GitHub Actions
Automate 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

Requirements

What you’ll need

Overall experience: 12-20+ years
6–8+ years of AI Engineer / Generative AI Engineer experience
10-15+ years of software engineering, cloud engineering, or AI/ML development experience
Strong Python 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
Experience with Claude Agent SDK, AI agent development, agent orchestration, and tool calling
Experience with Amazon Bedrock, Bedrock AgentCore Runtime, Step Functions, Textract, and Comprehend
Experience deploying Langfuse for LLM tracing, monitoring, and performance analysis
Experience with continuous agent evaluation, LLM-as-a-Judge, evaluation metrics, and automated testing
Experience with GitHub Actions, CI/CD pipelines, Git, and deployment automation
Experience with vector databases, embeddings, chunking strategies, indexing, and knowledge base integrations
Experience with AWS architecture, IAM, CloudWatch, security, and deployment practices
Good-to-have: LangChain, LangGraph, or other agent orchestration frameworks
Good-to-have: RAGAS, DeepEval, or similar LLM evaluation frameworks
Good-to-have: Amazon OpenSearch, Pinecone, FAISS, or pgvector
Good-to-have: Docker, Terraform, or infrastructure as code
Good-to-have: Model Context Protocol (MCP)
Good-to-have: prompt engineering, guardrails, and AI governance
Good-to-have: enterprise Generative AI production deployment experience
Must be comfortable traveling to the client site for workshops/meetings
Must be located in the western time zones of the USA

Benefits

Comp & perks

Remote/hybrid work arrangement
Travel to client sites for workshops/meetings