The Full Stack AI Engineer leads the technical delivery of agentic AI programs 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
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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.
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Design agent harnesses, orchestration topologies (supervisor/worker, event-driven, parallel), A2A coordination patterns, and LLM gateway configuration across LLM providers.
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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.
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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
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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.
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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.
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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.
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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
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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.
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Develop initial value hypotheses for agentic solutions — identifying automation and augmentation opportunities, estimating business impact, and establishing baseline metrics before delivery begins.
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Contribute to solution design and proposal development; identify expansion opportunities within current engagements and support account growth.
Delivery Excellence and AgentOps
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Lead workstream delivery in agile environments — managing scope, quality, technical risk, and milestone accountability with senior stakeholder visibility.
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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.
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Define and implement evaluation frameworks, agent testing suites, HITL feedback capture, and production observability — distributed tracing, cost tracking, latency profiling, and drift detection.
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Implement guardrails, prompt injection defences, agent identity scoping, PII redaction, and audit trail design for enterprise compliance.
Team Leadership and Development
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Lead, manage, and develop a team of Consultants and Analysts — setting clear expectations, providing technical coaching, and running structured code reviews and design sessions.
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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
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Maintain current, hands-on knowledge of agentic AI developments — testing new frameworks, tooling, and research; bringing relevant advances into the team's engineering practice.
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Contribute to internal practice development: reusable accelerators, reference implementations, and delivery standards that improve capability across Accenture's AI practice.
- Building LLM-based applications in production — with operational accountability for deployed systems.
- 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.
- AI/ML, data engineering, or advanced analytics — integrating intelligent systems into production software.
- full stack engineering: Python and a frontend framework (React, Angular, or Node.js); active, hands-on capability across the stack.
- 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.
- 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.