Agentic AI Engineer
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Job Summary
Tasks
Agentic AI Engineer
Duration: 3-4 months
Location: London
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 Engineering AWS Platform AIOps/LLMOps Dev
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.
Requirements
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.
