Full Stack Engineer (AI Lead) - #1774

Role OverviewWe are seeking an experienced AI Lead to define and drive our Agentic AI and Generative AI strategy on AWS. You will lead a team of AI engineers and developers in designing, building and operating production-grade multi-agent systems using Amazon Bedrock, Bedrock Guardrails and Amazon Bedrock AgentCore.This is a hands-on leadership role combining architecture ownership, responsible AI governance, stakeholder engagement and delivery management.Key ResponsibilitiesDefine the enterprise Agentic AI vision, reference architecture and roadmap on AWS.Lead and mentor a team of AI developers, ML engineers and prompt/agent engineers; own delivery, code quality and engineering standards.Architect multi-agent solutions covering planning, tool use, memory, orchestration and human-in-the-loop workflows.Own the selection, evaluation and cost/performance optimisation of foundation models available on Amazon Bedrock (e.g. Anthropic Claude, Amazon Nova, Meta Llama, Mistral).Establish responsible AI governance using Amazon Bedrock Guardrails: content filters, denied topics, word filters, PII redaction/sensitive information filters, contextual grounding checks and Automated Reasoning checks.Drive adoption of Amazon Bedrock AgentCore (Runtime, Gateway, Memory, Identity, Observability, Code Interpreter and Browser) as the standard platform for deploying and operating agents securely at scale.Define evaluation frameworks for agents and LLM applications (accuracy, groundedness, safety, latency, cost) and establish LLMOps / AgentOps practices.Partner with security, risk, legal and compliance teams to ensure AI solutions meet regulatory, data privacy and audit requirements.Engage business stakeholders to identify high-value use cases, build business cases and translate them into delivery plans.Evaluate emerging technologies and standards (e.g. Model Context Protocol, Agent-to-Agent protocol, Strands Agents SDK) and recommend adoption.Report on delivery progress, risks, adoption metrics and ROI to senior leadership.Required Skills & Experience10+ years in software engineering, data or AI/ML, including 3+ years leading technical teams.2+ years of hands-on experience designing and delivering Generative AI solutions, with at least 1 year in Agentic AI (multi-agent systems, tool calling, planning, memory).Proven track record of taking LLM/agent solutions from PoC to production in an enterprise environment.Deep working knowledge of Amazon Bedrock, including model access and invocation, Knowledge Bases (RAG), Bedrock Agents, Prompt Management, Flows, model evaluation and Provisioned Throughput.Strong experience implementing Bedrock Guardrails and broader AI safety patterns (prompt injection defence, jailbreak mitigation, PII protection, hallucination control).Hands-on experience with Amazon Bedrock AgentCore functionalities: Runtime, Gateway, Memory, Identity, Observability, Code Interpreter and Browser tools.Solid AWS architecture skills: IAM, VPC / PrivateLink, Lambda, API Gateway, Step Functions, S3, DynamoDB, CloudWatch, KMS, OpenSearch.Strong Python skills and familiarity with agent frameworks such as Strands Agents, LangChain / LangGraph, CrewAI or AutoGen.Technical ExpertiseAgentic AI: multi-agent orchestration, ReAct / plan-and-execute patterns, tool and function calling, MCP and A2A protocols.AWS Bedrock: foundation models, Knowledge Bases, Agents, Guardrails, Flows, Prompt Management, Model Evaluation.AgentCore: Runtime, Gateway, Memory (short- and long-term), Identity, Observability, Code Interpreter, Browser.RAG: embeddings, vector stores (OpenSearch, Aurora pgvector), chunking and retrieval optimisation, reranking.Governance & security: guardrail policy design, least-privilege IAM, data residency, audit logging, red-teaming.LLMOps / AgentOps: CI/CD, Infrastructure as Code (CDK / Terraform / CloudFormation), tracing, evaluation pipelines, cost monitoring.Preferred QualificationsAWS Certified Solutions Architect – Professional, AWS Certified Machine Learning – Specialty or AWS Certified AI Practitioner / Generative AI certification.Experience in a regulated industry (financial services, healthcare, public sector).Experience establishing an AI Centre of Excellence or internal AI platform.Contributions to open-source agent tooling, publications or conference talks.Soft SkillsStrong leadership, coaching and team-building ability.Excellent communication with both technical and executive audiences.Strategic thinking with a pragmatic, delivery-focused mindset.Sound judgement on AI risk, ethics and responsible use.EducationDegree in Computer Science, Engineering, Data Science, AI or a related field (or equivalent practical experience).