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<h2 id="tasks">Tasks</h2>
<p><strong>Agentic AI Engineer</strong></p>
<p><strong>Duration: 3-4 months</strong></p>
<p><strong>Location: London</strong></p>
<p><strong>Role Summary</strong></p>
<p>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.</p>
<p><strong>Required Skills</strong></p>
<p><strong>Agentic AI, Engineering, AWS Platform, AIOps/LLMOps, Dev</strong></p>
<p><strong>Key Responsibilities</strong></p>
<ol>
<li><strong>AI Agent Development</strong></li>
</ol>
<p>Design and develop AI agents and multi-agent workflows using AWS AgentCore and Amazon Bedrock.</p>
<p>Build autonomous and intelligent agents leveraging foundation models, tools, memory and orchestration capabilities.</p>
<p>Implement RAG, tool calling, agent collaboration patterns and workflow automation for enterprise use cases.</p>
<p>Integrate AI agents with enterprise APIs, databases, event streams and third-party platforms.</p>
<p>Design secure and scalable AI architectures aligned to AWS Well-Architected principles.</p>
<ol>
<li><strong>Application Engineering</strong></li>
</ol>
<p>Develop backend services, APIs and orchestration components using Python and TypeScript.</p>
<p>Build event-driven and serverless applications using AWS Lambda, API Gateway, EventBridge, Step Functions, DynamoDB and SQS/SNS.</p>
<p>Create reusable libraries, patterns and accelerators to standardize AI agent development across teams.</p>
<ol>
<li><strong>AIOps, Production Monitoring & Operations</strong></li>
</ol>
<p>Establish monitoring, observability and operational governance for production AI workloads.</p>
<p>Track agent performance, model latency, cost, prompt effectiveness, error rates and quality signals.</p>
<p>Define alerting, incident response, RCA processes and production runbooks for AI applications.</p>
<p>Troubleshoot AI agent issues across orchestration logic, integration failures, prompt/model behavior and platform dependencies.</p>
<p>Continuously optimize reliability, accuracy, latency and cost for production GenAI systems.</p>
<ol>
<li><strong>DevOps & Platform Collaboration</strong></li>
</ol>
<p>Build and maintain CI/CD pipelines for AI application and agent deployments.</p>
<p>Implement Infrastructure as Code using Terraform, AWS CDK or CloudFormation.</p>
<p>Collaborate with solution architects, platform teams, security teams and business stakeholders to deliver enterprise-grade AI solutions.</p>
<h2 id="requirements">Requirements</h2>
<p><strong>Required Skills & Qualifications</strong></p>
<p>Strong hands-on experience in Amazon Bedrock and agentic AI implementation patterns.</p>
<p>Practical experience with AWS AgentCore or similar AI agent runtime/orchestration capabilities.</p>
<p>Advanced Python and TypeScript development experience.</p>
<p>Experience implementing RAG, prompt engineering, tool/function calling and AI workflow orchestration.</p>
<p>Experience with production monitoring, observability and operational support for AI/ML or GenAI workloads.</p>
<p>Strong understanding of AWS serverless, event-driven architecture, IAM and cloud security principles.</p>
<p>Good understanding of CI/CD, Infrastructure as Code and release automation.</p>
<p>Strong problem-solving, communication and stakeholder collaboration skills.</p>
<p><strong>Preferred Skills</strong></p>
<p>Experience with LangChain, LangGraph, Semantic Kernel, CrewAI or similar agent frameworks.</p>
<p>Experience with Bedrock Knowledge Bases, vector databases such as OpenSearch, Pinecone or Weaviate, and embedding-based retrieval patterns.</p>
<p>Experience with MCP (Model Context Protocol), enterprise tool integration and workflow automation.</p>
<p>Knowledge of AI safety, guardrails, governance, responsible AI and GenAI FinOps.</p>
<p>Experience integrating AI solutions with ServiceNow, Salesforce, SAP or other enterprise systems.</p>
<p>Experience operating highly available AI applications in production environments.</p>
<p><strong>Certifications</strong></p>
<p>AWS Certified AI Practitioner – preferred.</p>
<p>AWS Certified Machine Learning Engineer – preferred.</p>
<p>AWS Certified Developer Associate – preferred.</p>
<p>AWS Certified Solutions Architect Associate or Professional – preferred.</p>
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