Senior AI Native Engineer

Description

The company builds AI systems that run in production inside large enterprise clients, not demos or pilots that stall. The work involves agentic architectures that operate within real client organizations.

This is a senior AI engineering role across the full enterprise technology stack. The work is hands-on delivery with client engineering teams, leading technical design sessions and creating reusable patterns and accelerators that carry across engagements.

The company works across every industry, enterprise technology stack and level of organizational complexity. It employs about 791,000 people and serves clients in more than 120 countries. Demand for AI engineers is rising faster than the supply of people with production experience. The role offers a route into the Forward Deployed Engineer programme.

Responsibilities

• Design and govern production agentic systems at enterprise scale, covering multi-agent orchestration, RAG pipelines, policy-based routing, memory management and lifecycle observability across a programme.
• Set RAG standards for all engagements, including chunking and embedding approaches and quality benchmarks, and document tradeoff decisions with metrics so others can reuse them.
• Define multi-LLM integration standards that default to vendor-agnostic design, with fallback routing and cost governance across OpenAI, Anthropic, Vertex AI and open-source models.
• Run LLMOps across several concurrent systems: eval strategy, prompt governance, observability tooling, safety monitoring and cost controls.
• Lead senior-level client engagements by running architecture design sessions, leading proof-of-concept delivery and aligning client technology leaders with delivery teams.
• Write and publish reusable patterns, accelerators and engineering standards that cut ramp-up time on new client work.
• Own the measurement framework for agentic quality, defining accuracy, latency, safety and cost metrics, and present AI impact in business terms to senior client stakeholders.

Requirements

• At least 8 years of software engineering experience in production environments.
• At least 1 year of hands-on experience designing and deploying agentic AI in production. This is a hard requirement.
• Production-level experience with agentic orchestration frameworks such as LangGraph, CrewAI, AutoGen or equivalent, beyond tutorial work.
• Direct experience calling LLM APIs from OpenAI, Anthropic or Vertex AI in production code, including provider abstraction, token management and latency and cost tradeoffs.
• Experience owning RAG pipelines: embeddings, chunking, vector databases and context engineering.
• Working knowledge of LLMOps: eval harness design, prompt versioning and production observability.
• Cloud-native engineering skills with Kubernetes, Docker, microservices, serverless, CI/CD and infrastructure as code using Terraform or Helm.
• Strong Python. Java or another backend language is acceptable. Experience debugging and observing production systems.
• Experience managing, developing and performance-managing a team of engineers, including setting individual development plans and holding career conversations.
• Quality of experience counts more than years. A candidate who has shipped three production agentic systems in four years is preferred over a generalist with only passive AI exposure.

Conditions

This role is tagged LI-EU.

Applicants are considered without regard to age, ethnicity, belief, colour, religion, sex, national origin, ancestry, disability, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship or any other legally protected characteristic.

Benefits

• Access to vendor fellowship programmes inside the engineering teams of Anthropic, OpenAI, Microsoft and Google.
• A direct pathway into the Forward Deployed Engineer programme.