Full Stack AI Engineer Senior 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 Senior Manager operates at the intersection of business transformation and advanced AI engineering. This role leads complex, multi-workstream agentic AI programmes — owning the transformation arc from business problem diagnosis through value hypothesis, solution architecture, and production delivery — while managing senior client stakeholders and driving account growth. Senior Managers operate as Forward Deployed Engineers at a senior level — able to enter a client environment, diagnose a complex business problem independently, design an end-to-end agentic AI solution, and lead its delivery to production. They are expected to maintain genuine technical authority — architecting systems, making consequential design decisions, and staying hands‑on with new developments in AI — while simultaneously leading teams, growing client relationships, and contributing to practice development. The ability to command credibility with a VP or C‑suite stakeholder in the morning and an engineering design session in the afternoon is the defining characteristic of this role.

Lead client discovery and visioning engagements — facilitating workshops that diagnose business processes, identify agentic AI opportunities, and define the target operating model.

Develop value hypotheses for agentic transformation: quantifying business cases in cost reduction, cycle time improvement, and revenue impact; establishing baselines before delivery; and tracking value realisation post-deployment.

Design the transformation roadmap from current‑state processes to agentic workflows — sequencing by value and feasibility, managing organisational change, and ensuring sustainable transition.

Own senior client relationships across active programmes — primary point of accountability for delivery performance, commercial outcomes, and strategic direction.

Drive the technical design and delivery of production agentic systems — architect agent harnesses, orchestration topologies (supervisor/worker, event‑driven, parallel), A2A coordination patterns, and LLM gateway configuration; make consequential design decisions and resolve complex engineering problems directly when required.

Define knowledge layer architecture across programmes: RAG pipeline design, MCP‑connected knowledge sources, Text‑to‑SQL, Elasticsearch integration, and knowledge graph and ontology layers — ensuring the right retrieval strategy is applied to each use case.

Establish evaluation and AgentOps standards: golden dataset governance, LLM‑as‑judge pipelines, trajectory evaluation, agent testing suites, CI/CD for agents and prompts, asset registry management, production observability, and drift detection — as engineering disciplines owned by the team, not afterthoughts.

Define trust, safety, and governance standards across programmes: guardrails, prompt injection defences, agent identity scoping, PII redaction, blast radius controls, HITL approval patterns, and audit trail design — aligned to client compliance requirements.

Define the end‑to‑end technical architecture for agentic AI programmes: agent design, orchestration, tool and knowledge layers, cloud infrastructure, data platform integration, and DevOps pipelines — to enterprise production standards. Remain hands‑on with AI developments — personally evaluating new frameworks, models, and tooling; contributing to architecture decisions and critical engineering problems directly when required. Oversee delivery across multiple concurrent workstreams — maintaining quality, managing risk, and ensuring measurable business outcomes are achieved and evidenced. Establish programme‑level DevOps, AgentOps, and LLMOps standards: CI/CD, evaluation gates, asset registry governance, deployment strategies, observability, and production operations.

Engage independently with senior client stakeholders — translating complex technical decisions into business‑oriented communication and building trusted advisory relationships at VP and C‑suite level. Lead programme steering committees, governance forums, and executive reviews — managing exp