About Accenture Data & AI

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.

About The Role

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.

Position Responsibilities

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.

Core Requirements

3+ years building LLM-based applications in production — with operational accountability for deployed systems.

2+ years designing and delivering agentic AI systems — agents operating with meaningful autonomy in real production environments.

Hands-on experience with at least one agent orchestration framework (LangGraph, AutoGen, CrewAI, AWS Strands, or equivalent) in production.

Demonstrated experience across the Agent Development Lifecycle: specification, harness build, tool and MCP integration, evaluation, deployment, observability, and refinement.

Proven track record deploying software systems in production with measurable results — reliability, performance, or business value outcomes.

Experience with knowledge layer engineering: RAG pipelines, MCP-connected sources, Text-to-SQL, Elasticsearch, and knowledge graph design.

6+ years experience in classical AI/ML, data engineering, or advanced analytics — integrating intelligent systems into production software.

7+ years full stack engineering: Python and a frontend framework (React, Angular, or Node.js); active, hands-on capability across the stack.

6+ years cloud-native development on AWS, Azure, or GCP — CI/CD, containerised workloads, infrastructure as code, and production operations.

Experience on complex digital transformation programmes — enterprise-scale, multi-workstream, client-facing delivery.

3+ years technical leadership: design ownership, code review, and engineer mentoring.

Experience engaging directly with client stakeholders — translating business requirements to technical solutions.

Bachelor's degree in a related field. A Master's degree is highly valued.

Additional Strong Signals

Experience facilitating client workshops to elicit requirements and design agentic solutions.

Experience developing value hypotheses and business cases for AI initiatives.

Experience with DevOps/AgentOps/LLMOps: CI/CD for agents, evaluation gates, asset registry management.

Experience with prompt versioning, A/B testing, and reasoning pattern selection in production.

Experience with guardrails, prompt injection defences, and compliance-focused audit trail design.

Experience with FinOps for agentic systems: token budgeting, cost-per-task tracking, model routing.

Experience integrating agentic systems with enterprise platforms (SAP, Salesforce, ServiceNow) via MCP or APIs.

Contributions to internal accelerators, reusable frameworks, or thought leadership in agentic AI.

Full Stack AI Engineer Manager