Full Stack AI Engineer Associate Manager

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 Accenture is establishing a dedicated Agentic AI Ninja Team — a group of highly experienced engineers tasked with solving the most complex challenges at the frontier of autonomous AI. This is a technical leadership position for engineers who have designed, built, and operated production-grade agentic systems at enterprise scale. The Associate Manager will be responsible for the full delivery lifecycle of agentic AI applications: from system architecture and agent design through to deployment, evaluation, and production observability. Candidates will be expected to bring deep hands-on experience with autonomous agent frameworks, multi-agent orchestration, advanced retrieval architectures, and enterprise-grade integration — not proof-of-concept or prototype experience, but systems that have operated under real business conditions with real consequences. This role also carries a technical leadership responsibility: guiding engineers, setting delivery standards, and owning the quality of output across fast-moving, high-visibility client engagements within Accenture's Data & AI practice. Position Responsibilities Agentic System Design and Delivery • Architect and deliver production-grade autonomous AI systems — agents that plan, reason, invoke tools, recover from failures, and integrate with enterprise backends across cloud platforms. • Select and apply appropriate reasoning patterns (ReAct, Chain-of-Thought, Tree-of-Thought, Plan-and-Execute, Reflexion) based on task complexity, latency requirements, and verifiability needs. • Author structured agent specifications using spec-driven development practices; apply AI-assisted engineering tooling (Claude Code, Codex) to accelerate delivery without compromising rigour. • Design and maintain prompt architecture for production agents — system prompt structure, few-shot example design, structured output schemas, prompt versioning, and A/B testing of prompt changes as production artefacts. Agent Harness and Orchestration • Design and implement the agent harness: agent instantiation, persona and instruction loading, tool binding, memory initialisation, and lifecycle management from invocation to termination. • Architect multi-agent orchestration topologies — supervisor/worker hierarchies, event-driven graphs, parallel execution — with defined A2A handoff contracts, shared state schemas, and structured escalation paths. • Configure the LLM gateway and model routing layer — directing agent calls by task type, latency, cost, and capability — using provider-agnostic abstraction (LiteLLM or equivalent) across LLM providers. Tool Layer, Context, and Memory • Design, build, and maintain MCP servers exposing enterprise systems, APIs, databases, and SaaS platforms as agent-accessible tools — with robust schema design, error handling, idempotency, and retry logic. • Translate business processes into agent-executable skills, structured instructions, and reusable workflows — bridging the gap between business requirements and agent implementation. • Build context engineering pipelines — assembling the right information into the agent context window across multi-turn and long-running tasks, with explicit management of context budget and retrieval triggers. • Implement memory architectures — episodic, working, and long-term — using appropriate backends (vector stores, relational databases, cache layers) matched to each agent use case. Knowledge Layer and Engineering • Design RAG pipelines for agentic contexts: hybrid search, semantic re-ranking, late chunking, multi-vector retrieval, and metadata filtering; manage the full lifecycle from ingestion through quality evaluation. • Build MCP-connected knowledge sources exposing structured and unstructured data assets as governed, agent-accessible tools. • Implement Text-to-SQL capabilities — prompt-to-query translation, schema grounding, query validation, and safe execution against live enterprise databases. • Integrate Elasticsearch as a retrieval backend: full-text search, BM25 scoring, faceted filtering, and hybrid semantic-lexical strategies. • Design knowledge graph and ontology layers providing agents with structured representations of domain entities and relationships for precise reasoning over interconnected enterprise knowledge. Agent Ops, Registry, and Observability • Operate and maintain production agentic systems using AgentOps, LLMOps, and DevOps practices — CI/CD pipelines for agent code and prompt changes, automated evaluation gates, and deployment strategies (blue/green, canary) across environments. • Manage an agent and asset registry — versioned catalogue of agents, tools, skills, prompts, and workflows — enabling reuse, governance, and controlled promotion across development, staging, and production. • Define and implement agent evaluation frameworks: golden dataset construction, LLM-as-judge pipelines, tool-call accuracy measurement, trajectory evaluation, and faithfulness scoring. • Build agent testing suites distinct from evals — unit testing agents with mocked tools, integration testing multi-agent handoffs, and simulation environments for pre-production scenario testing. • Design HITL feedback capture: structuring human corrections and approvals as refinement signal for continuous improvement. • Build production observability from day one — distributed tracing, t…