AI Native Software Engineering Manager

Description   You build the systems that actually make AI work in enterprise environments, not demos, not prototypes that stall after a pilot, but production agentic architectures running inside real client organizations. The difference between an AI Engineer and what we are looking for is straightforward: you have shipped a multi-agent system in production, you have owned the eval harness, and you know what happens when your agent fails at 2am because you have lived it.     As an AI Engineer (Agentic/Applied), you will design, build, and deploy production-grade agentic AI systems across the full enterprise technology stack. You will work directly with client engineering teams, lead technical design sessions, and build reusable patterns and accelerators that scale beyond individual engagements.     This role sits at the heart of the AI engineering talent market — demand is growing faster than supply and will continue to do so. We offer what no single product company can: breadth across every industry, every enterprise technology stack, and every level of organizational complexity, combined with vendor fellowship access inside Anthropic, OpenAI, Microsoft, and Google engineering teams and a direct pathway to the Forward Deployed Engineer   programme .

Architect and govern production-grade agentic systems at enterprise scale: multi-agent orchestration across complex environments, RAG pipelines, policy-based routing, memory management, and   programme -level lifecycle observability   Define RAG pipeline standards across engagements: establish chunking and embedding strategies, set quality benchmarks, and ensure metric-backed tradeoff decisions are documented and transferable   Set multi-LLM integration standards: vendor-agnostic architecture by default, fallback routing and cost governance as standard design practice across providers including OpenAI, Anthropic, Vertex AI, and open-source models   Own   LLMOps   at   programme   scale: eval strategy, prompt governance, observability tooling standards, safety monitoring and cost controls across multiple concurrent systems   Lead client engineering engagements at senior level — facilitate architecture design sessions, lead proof-of-concept delivery, and drive alignment between client technology leadership and delivery teams   Shape and publish reusable patterns, accelerators, and engineering standards that scale across the practice and reduce ramp-up time on new client engagements   Own the measurement framework for agentic system quality: define accuracy, latency, safety, and cost metrics; present   programme -level AI impact in business terms to senior client stakeholders     Basic Qualifications   8+ years of software engineering experience in production environments   Minimum 1 year of hands-on experience designing and deploying agentic AI solutions in a production environment — non-negotiable   Demonstrated experience with agentic orchestration frameworks:   LangGraph ,   CrewAI ,   AutoGen , or equivalent — at production depth, not tutorial level   Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs   RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering   LLMOps   fundamentals: eval harness design, prompt versioning, and production observability   Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and   IaC   (Terraform or Helm)   Strong Python; Java or equivalent backend language acceptable; production debugging and observability experience   Quality of experience is weighted over years, a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure   8-10 years of experience leading software engineering teams: overseeing delivery, allocating