Forward Deployed AI Engineer

The Company

We’re partnering with a technology organisation that is scaling its enterprise AI engineering capability. The team builds and deploys production AI systems into complex enterprise environments, solving problems where reliability, integration and measurable business impact matter as much as model capability.

This is not a research or prototype-focused environment. The emphasis is on getting modern AI systems working reliably in production alongside existing applications, data and infrastructure.

The Role

You’ll work directly with enterprise customers to design, build and deploy advanced AI systems against real operational problems.

This is a hands-on engineering role. You’ll own the technical journey from discovery and architecture through implementation, integration and production deployment, working closely with customer subject-matter experts and internal engineering teams.

You’ll be responsible for:

Designing and deploying production-grade agentic AI systems

Building stateful workflows, tool-using agents and MCP-based integrations

Connecting modern AI systems with existing enterprise applications and data

Working through cloud, networking and security constraints to get systems into production

Building robust RAG, retrieval and context-management architectures

Developing evals, guardrails and fallback strategies for non-deterministic AI systems

Working directly with users and subject-matter experts to understand workflows and iterate on solutions

Identifying reusable engineering patterns that can accelerate future AI deployments

What We’re Looking For

Strong software engineering background with advanced TypeScript / Node.js and Python

Experience designing distributed, event-driven or stateful production systems

Hands-on experience building production applications with OpenAI, Anthropic or equivalent frontier-model APIs

Experience with agentic architectures, tool use/function calling and MCP

Strong understanding of RAG, embeddings, vector search and context engineering

Experience building evals, guardrails, reliable fallbacks and CI/CD around AI systems

Strong cloud engineering experience across Azure and/or AWS

Good understanding of enterprise identity, security and access-control requirements

Experience integrating software with enterprise applications, APIs and legacy systems

Strong communication skills and confidence working directly with technical and non-technical customer stakeholders

Comfortable operating in ambiguous environments where the technical path is not already defined

Experience with Go, Rust or complex enterprise integrations is desirable

Previous forward-deployed, implementation engineering or other customer-facing technical experience is desirable

What’s in It for You?

Build production AI systems rather than proof-of-concepts

Work on complex, high-impact problems inside major enterprise environments

Combine hands-on software engineering with applied AI and direct customer ownership

Gain exposure to a wide range of architectures, systems and business use cases

Help shape reusable frameworks and engineering approaches as the AI capability scales

Progress into deeper technical leadership and increasingly complex delivery ownership

This is a senior, hands-on engineering role for someone who is equally comfortable designing distributed systems, writing production code and working directly with customers.