Forward Deployed Engineer (FDE) - R&D AI Services

About the role

IFS is hiring a Forward Deployed Engineer for its R&D AI Services team. The FDE embeds directly with product development teams to understand real business problems and solve them with AI, either by reusing or extending an existing AI service or by scoping and building a new one. It is a high-agency, hands-on senior engineer role bridging product business flows and working AI capabilities.

Key responsibilities

• Embed with product teams to understand their business flows and identify where AI genuinely helps
• Separate the real bottleneck in a business process from the symptom brought to the product team
• Use the existing AI service catalogue to match services to problems and help teams build AI-first applications on top of them
• When no service fits, gather and document requirements and work with the engineering team to scope, design and build the new service, staying hands-on throughout
• Work hands-on with data pipelines, model training and serving, MLOps tooling and integrations
• Ship a working first version quickly, then harden it with monitoring, feedback loops and production-grade reliability
• Turn product pain points into input for the AI services roadmap and communicate trade-offs clearly to non-technical audiences

Required profile

• 5+ years of software engineering experience, with strong hands-on capability building and deploying production systems
• Ability to work across an unfamiliar stack when needed
• Strong Python, with willingness to work across other languages and customer codebases
• Experience building and owning production ML/data systems, not just notebooks or prototypes
• Able to turn ambiguous business problems into scoped, shipped systems and to work alongside platform or infrastructure teams

Required skills

• Cloud and Kubernetes: experience building and operating cloud-native services; Azure (AKS, Blob Storage, Key Vault, Azure-hosted AI/model services) highly relevant
• AI and LLM integration: model APIs, authentication, gateways, reliability, latency, cost and observability
• Agentic systems and hands-on MCP experience strongly preferred
• RAG and retrieval systems, including embeddings, vector databases, indexing and evaluation
• REST APIs, authentication/authorization, data contracts and debugging complex integrations
• Docker, Kubernetes, Helm, GitOps/ArgoCD and Terraform or equivalent infrastructure-as-code
• Data pipelines and relational, document, vector and object stores