Forward Deployed Engineer (FDE)

IFS

2d•Hybrid

About The Position

As a Forward Deployed Engineer in R&D AI Services, you embed directly with product development teams to identify business problems that AI can solve. You connect product teams’ business flows with working AI capabilities by selecting and extending existing AI services, helping teams build AI-first applications, and, where there is a gap, gathering requirements and partnering with the AI services engineering team to build a suitable service. This is a hands-on, high-agency role involving stakeholder engagement as well as building and shipping data pipelines, model-serving systems, and integrations. You work across discovery, implementation, and production hardening: identifying process bottlenecks, assessing AI service options, building or extending solutions, and adding monitoring, feedback loops, and production-grade reliability. You also provide field insights that inform the AI services roadmap. The role is based in a flexible, hybrid work environment, with opportunities to work directly with product owners, business stakeholders, engineering leads, and customers.

Requirements

At least 5 years of software engineering experience.
Strong hands-on capability building and deploying production systems, and ability to work across unfamiliar technology stacks.
Strong Python skills and the ability and willingness to work in other languages and existing customer codebases.
Experience building and operating cloud-native services.
Experience integrating LLMs and AI services into production applications, including model APIs, authentication, gateways, reliability, latency, cost, and observability.
Experience building agentic applications using tools, APIs, and orchestration frameworks.
Hands-on experience designing production RAG and retrieval systems, including embeddings, vector databases, indexing, retrieval quality, and evaluation.
Strong experience with REST APIs, authentication and authorization, external systems, data contracts, and debugging complex integrations.
Experience with Docker, Kubernetes, Helm, GitOps/ArgoCD, and Terraform or equivalent infrastructure-as-code tooling.
Comfort working with data pipelines and relational, document, vector, and object stores as required by a solution.
Solid understanding of event-driven and distributed-systems architecture, applied pragmatically in delivery.
Understanding of AI evaluation, observability, monitoring, failure modes, and trade-offs among quality, latency, reliability, and cost.
Experience building and owning production ML or data systems, rather than only notebooks or prototypes.
Ability to work through ambiguity and turn an unclear business need into a scoped, shipped system.
Ability to communicate effectively with non-technical stakeholders while preserving technical substance.
Experience working in or alongside platform or infrastructure teams.
Ability to work directly with customers and stakeholders, translate ambiguous business problems into technical solutions, prototype rapidly, and productionize solutions.
Demonstrable hands-on experience designing, building, and shipping production AI applications; using AI coding assistants alone does not meet this requirement.

Nice To Haves

Azure experience, particularly AKS, Blob Storage, Key Vault, and Azure-hosted AI or model services.
Hands-on MCP experience.

Responsibilities

Embed with product teams to understand business flows and identify where AI can help.
Lead technical discovery to distinguish process bottlenecks from reported symptoms.
Identify existing AI services that can be applied or extended, and help teams design and build AI-first applications using them.
Gather and document requirements when no existing AI service fits, then partner with the responsible engineering team to scope, design, and build a new service.
Work hands-on with data pipelines, model training and serving, MLOps tooling, and integrations into product workflows.
Ship an initial working version quickly, then add monitoring, feedback loops based on real usage, and production-grade reliability.
Translate product team pain points and business requirements into actionable input for the AI services roadmap.
Communicate trade-offs clearly to technical and non-technical stakeholders, including product owners, business stakeholders, and engineering leads.
Connect IFS R&D AI capabilities with the product teams using them, sharing field insights to influence future development.
Work directly with customers and stakeholders to turn ambiguous business problems into technical solutions, prototype rapidly, and productionize solutions.