AWS AI Agent Engineer

AWS AI Agent Engineer

Duration: 3-4 months 

Location: London 

Rate: £400 GBP/Day (Outside IR 35) 

Role Summary

We are seeking a highly skilled AWS AI Agent Engineer with strong hands-on experience in agentic AI development, Amazon Bedrock, AWS AgentCore, Python, TypeScript and production-grade AIOps. The role will focus on designing, building, deploying and operating enterprise AI agents and multi-agent workflows on AWS, with strong emphasis on observability, reliability, cost control, security and continuous optimization in production environments.

 Required Skills

Agentic AI

AWS AgentCore, Amazon Bedrock, multi-agent workflows, tool calling, memory, orchestration, RAG, prompt engineering

Build enterprise-grade AI agents that can reason, act, retrieve knowledge and integrate with business systems.

Engineering

Advanced Python, TypeScript, REST APIs, serverless development, reusable frameworks, secure coding practices

Deliver scalable backend services and reusable accelerators for AI workloads.

AWS Platform

Lambda, API Gateway, Step Functions, EventBridge, DynamoDB, S3, SQS/SNS, IAM, CloudWatch

Deploy secure, event-driven and resilient AI-powered applications on AWS.

AIOps / LLMOps

Production monitoring, model/agent telemetry, latency, cost, hallucination tracking, prompt evaluation, incident management

Operate AI agents in production with observability, governance and continuous improvement.

DevOps

CI/CD, Terraform/CDK/CloudFormation, GitHub/GitLab, Docker, release automation

Automate deployment, environment management and release governance.

  Key Responsibilities

AI Agent Development

Design and develop AI agents and multi-agent workflows using AWS AgentCore and Amazon Bedrock.

Build autonomous and intelligent agents leveraging foundation models, tools, memory and orchestration capabilities.

Implement RAG, tool calling, agent collaboration patterns and workflow automation for enterprise use cases.

Integrate AI agents with enterprise APIs, databases, event streams and third-party platforms.

Design secure and scalable AI architectures aligned to AWS Well-Architected principles.

Application Engineering

Develop backend services, APIs and orchestration components using Python and TypeScript.

Build event-driven and serverless applications using AWS Lambda, API Gateway, EventBridge, Step Functions, DynamoDB and SQS/SNS.

Create reusable libraries, patterns and accelerators to standardize AI agent development across teams.

AIOps, Production Monitoring & Operations

Establish monitoring, observability and operational governance for production AI workloads.

Track agent performance, model latency, cost, prompt effectiveness, error rates and quality signals.

Define alerting, incident response, RCA processes and production runbooks for AI applications.

Troubleshoot AI agent issues across orchestration logic, integration failures, prompt/model behavior and platform dependencies.

Continuously optimize reliability, accuracy, latency and cost for production GenAI systems.

DevOps & Platform Collaboration

Build and maintain CI/CD pipelines for AI application and agent deployments.

Implement Infrastructure as Code using Terraform, AWS CDK or CloudFormation.

Collaborate with solution architects, platform teams, security teams and business stakeholders to deliver enterprise-grade AI solutions.

Required Skills & Qualifications

Strong hands-on experience in Amazon Bedrock and agentic AI implementation patterns.

Practical experience with AWS AgentCore or similar AI agent runtime/orchestration capabilities.

Advanced Python and TypeScript development experience.

Experience implementing RAG, prompt engineering, tool/function calling and AI workflow orchestration.

Experience with production monitoring, observability and operational support for AI/ML or GenAI workloads.

Strong understanding of AWS serverless, event-driven architecture, IAM and cloud security principles.

Good understanding of CI/CD, Infrastructure as Code and release automation.

Strong problem-solving, communication and stakeholder collaboration skills.

Preferred Skills

Experience with LangChain, LangGraph, Semantic Kernel, CrewAI or similar agent frameworks.

Experience with Bedrock Knowledge Bases, vector databases such as OpenSearch, Pinecone or Weaviate, and embedding-based retrieval patterns.

Experience with MCP (Model Context Protocol), enterprise tool integration and workflow automation.

Knowledge of AI safety, guardrails, governance, responsible AI and GenAI FinOps.

Experience integrating AI solutions with ServiceNow, Salesforce, SAP or other enterprise systems.

Experience operating highly available AI applications in production environments.

Certifications

AWS Certified AI Practitioner – preferred.

AWS Certified Machine Learning Engineer – preferred.

AWS Certified Developer Associate – preferred.

AWS Certified Solutions Architect Associate or Professional – preferred.