Full Stack Engineer (AI Lead) - #1774

Role Overview

We 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 Responsibilities

• Define 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 & Experience

• 10+ 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 Expertise

• Agentic 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 Qualifications

• AWS 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 Skills

• Strong 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.

Education

Degree in Computer Science, Engineering, Data Science, AI or a related field (or equivalent practical experience).