Full Stack AI Engineer Manager

The beginning of a new Data & AI decade that will reshape work and society is underway. Accenture is stepping boldly into this future with a clear strategy and purpose: to help clients optimise and reinvent their businesses with data and AI — backed by a $3 billion investment and a commitment to industry-defining work.

With over 45,000 professionals dedicated to Data & AI, Accenture's Data & AI organisation brings together Experienced Innovation, Strategic Investment, Exceptional Talent, and a Power Ecosystem to deliver outcomes at the frontier of what is possible.

The Full Stack AI Manager leads the technical delivery of agentic AI programmes while remaining an active, hands-on engineer. This role bridges the gap between business problems and technical solutions — working directly with clients to understand requirements, translating them into agentic application designs, and leading a team of engineers to deliver production-grade systems that generate measurable value.

Managers on this team operate as Forward Deployed Engineers — brought into client environments to rapidly understand a business problem, design a full stack AI solution, and build it. The expectation is genuine technical depth combined with client credibility: the Manager must be as effective in a code review or design session as they are in a client workshop. They develop their team, grow client relationships, and continuously evolve their knowledge of agentic AI as the field advances.

Technical Solution Design and Hands-On Delivery

• Architect and deliver agentic AI solutions end-to-end — agents, orchestration, tool layers, knowledge pipelines, and full stack applications — at production engineering standards, contributing directly to code and design when required.

• Design agent harnesses, orchestration topologies (supervisor/worker, event-driven, parallel), A2A coordination patterns, and LLM gateway configuration across LLM providers.

• Build and maintain MCP servers, translate business processes into agent skills and reusable workflows, and implement advanced knowledge layer components: RAG, Text-to-SQL, Elasticsearch, knowledge graphs.

• Establish prompt architecture standards — versioning, A/B testing, structured output schemas — and apply reasoning patterns (ReAct, CoT, ToT) appropriate to each agent use case.

Agentic AI Technical Delivery

• Architect and build production agentic systems hands-on — agent harnesses, orchestration topologies (supervisor/worker, event-driven, parallel), A2A coordination patterns, and LLM gateway configuration; write code, resolve complex engineering problems, and set the quality standard through personal example.

• Design and implement knowledge layer components: RAG pipelines (hybrid search, re-ranking, late chunking), MCP-connected knowledge sources, Text-to-SQL, Elasticsearch integration, and knowledge graph layers — selecting and tuning the right retrieval strategy per use case.

• Build and operate evaluation and AgentOps pipelines: golden datasets, LLM-as-judge, trajectory evaluation, agent testing suites (unit, integration, simulation), CI/CD for agents and prompts, asset registry management, production observability, and drift detection.

• Implement trust, safety, and governance components: guardrails, prompt injection defences, agent identity scoping, PII redaction, blast radius controls, HITL approval gates, and audit trail design for enterprise compliance.

Client Engagement and Business Translation

• Serve as the primary technical point of contact for client stakeholders — running workshops, translating business requirements into agent solution designs, and communicating technical decisions clearly to non-technical audiences.

• Develop initial value hypotheses for agentic solutions — identifying automation and augmentation opportunities, estimating business impact, and establishing baseline metrics before delivery begins.

• Contribute to solution design and proposal development; identify expansion opportunities within current engagements and support account growth.

Delivery Excellence and AgentOps

• Lead workstream delivery in agile environments — managing scope, quality, technical risk, and milestone accountability with senior stakeholder visibility.

• Establish DevOps, AgentOps, and LLMOps practices: CI/CD for agent code and prompts, automated evaluation gates, deployment strategies, agent and asset registry management, and production operations.

• Define and implement evaluation frameworks, agent testing suites, HITL feedback capture, and production observability — distributed tracing, cost tracking, latency profiling, and drift detection.

• Implement guardrails, prompt injection defences, agent identity scoping, PII redaction, and audit trail design for enterprise compliance.

Team Leadership and Development

• Lead, manage, and develop a team of Consultants and Analysts — setting clear expectations, providing technical coaching, and running structured code reviews and design sessions.

• Foster a delivery culture of engineering rigour, continuous improvement, and learning — supporting team members in building agentic AI depth through challenging work and active knowledge sharing.

Innovation and Continuous Learning

• Maintain current, hands-on knowledge of agentic AI developments — testing new frameworks, tooling, and research; bringing relevant advances into the team's engineering practice.

• Contribute to internal practice development: reusable accelerators, reference implementations, and delivery standards that improve capability across Accenture's AI practice.