Full Stack AI Engineer

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 • 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. ■ Building LLM-based applications in production — with operational accountability for deployed systems. ■ D esigning 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 sof…