As a Forward Deployed Engineer, R&D AI Services, you embed directly with product development teams to understand real business problems and work out the right way to solve them with AI: reusing and extending an existing AI service, or — when nothing suitable exists — scoping the requirements and working with the engineering team to build one. You are the bridge between a product team's business flow and a working AI capability: you know the AI service catalogue well enough to match the right service to the problem, you help teams build AI-first applications on top of it, and when a gap exists, you make sure the right thing gets built to close it.
This is a high-agency role for a hands-on senior engineer who is as comfortable in a stakeholder conversation about a business process as they are building and shipping the data pipeline or model-serving system behind it.
What you'll do
- 5+ years of software engineering experience, with strong hands-on capability building and deploying production systems and the ability to work across an unfamiliar stack when needed.
- Languages: Strong Python, with the ability and willingness to work across other languages and existing customer codebases.
- Cloud & Kubernetes: Experience building and operating cloud-native services. Azure experience - particularly AKS, Blob Storage, Key Vault, and Azure-hosted AI/model services - is highly relevant.
- AI & LLM Integration: Experience integrating LLMs and AI services into production applications, including model APIs, authentication, gateways, reliability, latency, cost, and observability.
- Agentic Systems: Experience building agentic applications using tools, APIs, and orchestration frameworks. Hands-on MCP experience is strongly preferred.
- Retrieval & RAG: Hands-on experience designing production RAG and retrieval systems, including embeddings, vector databases, indexing, retrieval quality, and evaluation.
- APIs & Enterprise Integration: Strong experience with REST APIs, authentication/authorization, external systems, data contracts, and debugging complex integrations.
- Deployment & IaC: Docker, Kubernetes, Helm, GitOps/ArgoCD, and Terraform or equivalent infrastructure-as-code tooling.
- Data & Storage: Comfortable working with data pipelines and across relational, document, vector, and object stores as required by the solution.
- Solid understanding of event-driven and distributed systems architecture, applied pragmatically in delivery contexts rather than academically
- Understanding of AI evaluation, observability, monitoring, failure modes, and the trade-offs between quality, latency, reliability, and cost.
- Experience building and owning production ML/data systems (not just notebooks or prototypes),
- You thrive in ambiguity - you can turn "this business flow feels like it needs AI" into a scoped, shipped system without waiting to be told exactly what to build
- You communicate well with non-technical stakeholders without dumbing down the substance
- Experience working in or alongside platform or infrastructure teams
- Able to work directly with customers and stakeholders, turn ambiguous business problems into technical solutions, rapidly prototype, and then productionize those solutions.
We embrace flexibility and hybrid work opportunities to support diverse needs and lifestyles, while also valuing inclusive workplace experiences. By fostering a sense of community, we drive innovation, strengthen connections, and nurture belonging. Our commitment ensures you can work in a way that suits you best, while also engaging with colleagues to share ideas and build meaningful relationships.