Responsibilities
- Architect and implement AI agents and multi-agent systems using AWS AgentCore and Amazon Bedrock.
- Develop intelligent, self-directed agents by leveraging foundation models, external tools, memory systems, and orchestration logic.
- Apply retrieval-augmented generation (RAG), tool invocation, agent coordination strategies, and workflow automation for business applications.
- Connect AI agents to enterprise systems including APIs, databases, event streams, and third-party services.
- Design secure, scalable AI infrastructures following AWS Well-Architected Framework guidelines.
- Create backend services, APIs, and workflow components using Python and TypeScript.
- Develop event-driven, serverless solutions using AWS Lambda, API Gateway, EventBridge, Step Functions, DynamoDB, and SQS/SNS.
- Produce reusable code libraries, design patterns, and development accelerators to standardize AI agent implementation across teams.
- Implement monitoring, observability, and operational controls for AI workloads in production.
- Monitor key metrics including agent performance, model response time, cost, prompt effectiveness, error rates, and output quality.
- Set up alerting systems, incident response protocols, root cause analysis procedures, and operational runbooks for AI applications.
- Diagnose and resolve issues across agent orchestration, integrations, prompt or model behavior, and platform dependencies.
- Continuously improve the reliability, accuracy, speed, and cost-efficiency of generative AI systems in production.
- Develop and manage CI/CD pipelines for automated deployment of AI agents and applications.
- Implement infrastructure provisioning through code using Terraform, AWS CDK, or CloudFormation.
- Work closely with architects, platform engineers, security teams, and business units to deliver robust enterprise AI solutions.
Work Arrangement
On-site — London
Other
Duration: 3-4 months
