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.

Client Transformation and Value Engineering

• 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.

Agentic AI Technical Delivery

• 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.

Solution Architecture and Programme Delivery

• 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.

Senior Stakeholder Management

• 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 expectations, escalations, and strategic direction with confidence.

• Represent Accenture's agentic AI capability in client forums and external engagements; articulate the strategic value of autonomous AI to business and technology leadership audiences.

Account Growth and Business Development

• Identify and develop expansion opportunities within current engagements — from delivery relationship to strategic advisory relationship.

• Contribute to proposals, solution designs, and commercial development for new and adjacent client opportunities; participate actively in pursuit efforts.

• Support the development of commercial proposals and business cases for agentic AI transformation programmes, including pricing, scope, and delivery approach.

Team and Practice Leadership

• Lead a team of Managers, Associate Managers, and engineers — providing technical and professional coaching, managing performance, and creating conditions for high-quality delivery.

• Drive internal practice development: reference architectures, delivery frameworks, value engineering tools, and thought leadership that advances Accenture's agentic AI capability and market positioning.

• Champion a culture of continuous learning — ensuring the team stays current with AI developments and that innovation flows from research into client delivery.

Core Requirements

• 3+ years building and operating agentic AI systems in production — autonomous agents at enterprise scale with demonstrable business value delivered.

• 3+ years building LLM-based applications — RAG, tool-calling, prompt architecture, evaluation — in production environments.

• Deep hands-on understanding of the full agentic AI technical stack: harness, orchestration, MCP/tools, knowledge layer, evaluation, trust and safety, AgentOps — able to architect, direct, and personally contribute across all components.

• Experience leading client-facing business transformation programmes involving AI — value hypothesis development, process reengineering, and outcome measurement.

• 8+ years experience in classical AI/ML, data engineering, and advanced analytics — delivering intelligent systems in complex enterprise environments.

• 10+ years of full stack engineering and complex software delivery — sustained technical depth with active, current engagement in agentic AI developments.

• 8+ years cloud-native development on AWS, Azure, or GCP — architecture, infrastructure management, DevOps, and production operations at programme scale.

• 5+ years technical leadership across multi-workstream programmes with delivery and quality accountability.

• Demonstrated experience managing senior client stakeholders — VP level or above — independently.

• Track record on large-scale, commercially significant digital transformation programmes.

• Bachelor's degree in a related field. A Master's degree in Computer Science, AI, or Engineering is highly valued.

Additional Strong Signals

• Experience establishing evaluation frameworks and baselining disciplines from scratch — not just running evals but institutionalising the practice.

• Experience with agent autonomy governance — blast radius, HITL gates, audit trails — in regulated enterprise environments.

• Experience contributing to commercial development: proposals, solution scoping, pricing, and pursuit leadership.

• Experience in regulated industries (financial services, healthcare, telecoms) where compliance shapes AI architecture.

• Experience with multi-LLM orchestration, FinOps for agents, and cost-governance at programme scale.

• Experience integrating agentic systems with complex enterprise platforms (SAP, Salesforce, ServiceNow, data lakes) in production.

• Published thought leadership, conference contributions, or open source work in agentic AI.

• Experience operating as a trusted technical advisor to CXO stakeholders on AI strategy and investment decisions.