AI Native SW Engineering Manager
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Read on to find out what you will need to succeed in this position, including skills, qualifications, and experience.
In this role you design, build, and deploy production-grade agentic AI systems across enterprise tech stacks. You’ll collaborate with client engineering teams to drive architecture and deliver reusable patterns that scale beyond single engagements. The position sits at the core of a growing AI engineering practice, offering exposure to multiple industries and direct access to senior engineering ecosystems. You will own the evaluation framework, observability, and performance metrics that prove business impact at scale.
Compensation / Benefits
- fellowship access inside Anthropic, OpenAI, Microsoft, and Google engineering teams
- direct pathway to the Forward Deployed Engineer programme
Responsibilities
- Architect and govern production-grade agentic systems at enterprise scale, including multi-agent orchestration, RAG pipelines, policy routing, memory management, and lifecycle observability
- Define RAG pipeline standards with chunking, embedding strategies, quality benchmarks, and measurable tradeoffs
- Set multi-LLM integration standards with vendor-agnostic architecture, fallback routing, and cost governance across providers
- Own LLMOps at programme scale, including eval strategy, prompt governance, observability tooling, safety monitoring, and cost controls
- Lead client engineering engagements at senior level, driving architecture sessions, PoCs, and alignment between client tech leadership and delivery teams
- Shape and publish reusable patterns, accelerators, and standards that scale across the practice and reduce ramp-up time
- Own the measurement framework for agentic system quality: accuracy, latency, safety, and cost metrics; articulate AI impact to senior stakeholders
Key requirements
- Extensive software engineering experience in production environments
- Hands-on experience designing and deploying agentic AI solutions in production
- Experience with agentic orchestration frameworks (LangGraph, CrewAI, AutoGen, or equivalent) at production depth
- Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code with provider abstraction, xhuatnn token management, latency and cost tradeoffs
- RAG pipeline ownership (embeddings, chunking, vector databases, context engineering)
- LLMOps fundamentals (eval harness design, prompt versioning, production observability)
- Cloud-native engineering maturity (Kubernetes, Docker, microservices, serverless, CI/CD, IaC like Terraform or Helm)
- Strong Python; Java or equivalent backend language; production debugging and observability
- Experience managing engineers and leading teams; performance management and career development
- client-facing communication
- leadership and mentorship
- architecture design facilitation
- LangGraph
- CrewAI
- AutoGen
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