Field AI Scheduling & Optimization Engineer

IFS

Staines-upon-Thames

Hybrid

GBP 90,000 - 120,000

Full time

13 days ago

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Job summary

IFS is seeking a Forward Deployed Engineer to embed with customers on our enterprise scheduling and optimisation platform (.NET 10/C# backend, Angular frontend) to solve complex resource planning at scale. This hands-on role combines software engineering, applied optimisation and direct customer engagement with AI-first workflows and rapid iteration in ambiguity.

The role suits an engineer who enjoys solving hard optimisation problems, working with customers, and feeding learnings back into the

Qualifications

  • Strong software engineering background; hands-on AI systems experience preferred.
  • Experience shipping AI-powered products or systems with production readiness.
  • Ability to work directly with customers and translate business problems into technical solutions.

Responsibilities

  • Translate real-world scheduling and planning problems into optimisation models.
  • Configure, extend, and debug the core scheduling engine against live datasets.
  • Build and iterate on new solutions with customer teams under tight deadlines.
  • Feed field learnings back to core product and engineering teams.
  • Engage with customers to gather requirements, explain trade-offs, and present results.
  • Ship working solutions that meet performance targets and improve the platform.

Skills

C#/.NET
Angular
TypeScript
Git
DevOps
Data modelling
Optimization
Constraint programming
Customer engagement

Tools

Git

Job description

IFS is seeking a Forward Deployed Engineer to embed with customers on our enterprise scheduling and optimisation platform (.NET 10/C# backend, Angular frontend) to solve complex resource planning at scale. This hands-on role combines software engineering, applied optimisation and direct customer engagement with AI-first workflows and rapid iteration in ambiguity.

The role suits an engineer who enjoys solving hard optimisation problems, working with customers, and feeding learnings back into the

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