Forward Deploy Engineer || IDP & GTM || AI & Agentic

About the role

This role sits at the intersection of platform, go-to-market, and product engineering where you will act as the technical face of IFS in customer environments and internal platform initiatives. You will design, build, and operate production-grade solutions that combine infrastructure, AI, and agentic capabilities using modern cloud-native stacks. A core part of your work will involve enabling internal platform teams and external customers by delivering reliable, observable, and scalable systems in Staines-upon-Thames and for global clients. You will translate complex platform concepts into clear narratives for both technical and non-technical audiences while maintaining a strong focus on shipping real features. This position requires comfort with ambiguity as you turn strategic product ideas into functioning systems that demonstrate clear business value. You will spend significant time building directly on the stack rather than only coordinating, ensuring deep insight into operational behavior and performance. Success in this role will be measured by your ability to own end-to-end outcomes and build trusted relationships across engineering and commercial teams.

Key facts

What you'll do

Assess customer environments and operational constraints to design tailored deployment and integration strategies that align with IFS product goals.

Implement and maintain production-ready infrastructure using Kubernetes, containers, GitOps, and Infrastructure as Code to support scalable delivery.

Collaborate closely with platform and infrastructure teams to ensure that deployed solutions meet enterprise standards for reliability and security.

Develop and evolve event-driven architectures using Apache Kafka or RedPanda to enable responsive and resilient data flows across services.

Build and own production AI applications and agentic workflows, ensuring they operate reliably in customer contexts beyond experimental prototypes.

Debug complex issues in live production systems, using observability data to isolate root causes and drive remediation.

Engage with non-technical stakeholders to clarify requirements, communicate trade-offs, and maintain transparency without oversimplifying technical risk.

Contribute to the design of internal developer platforms that abstract complexity and accelerate delivery for product teams.

Demonstrate hands-on experience designing, building, and operating intelligent systems at scale, including evidence of working with real AI workloads in production.

Translate ambiguous business problems into scoped technical solutions, iterating quickly and delivering working systems without waiting for explicit direction.

Support sales and pre-sales activities by providing technical validation, proofs of concept, and architecture reviews for prospective customers.

Work within a hybrid schedule, balancing independent focus with collaborative sessions to align with team rhythms and stakeholder needs.

Continuously refine deployed systems based on usage data and feedback to improve performance, adoption, and long-term reliability.

Act as an integration specialist across data, messaging, and application layers to ensure coherent end-to-end behavior.

Represent IFS technical practices in cross-functional forums, sharing insights that influence platform roadmaps and product decisions.

Maintain strong technical hygiene through code reviews, testing, and documentation that supports maintainability and on-call efficiency.

Requirements

Bring 5+ years of software development experience with strong hands-on capability across the stack: Backend in Go, Frontend in React with Next.js, Messaging/Streaming with Apache Kafka / RedPanda, and Data in PostgreSQL.

Comfortably operate in a cloud-native environment using Kubernetes, containers, GitOps, IaC, and Software Defined Infrastructure.

Apply a solid understanding of event-driven and distributed systems architecture pragmatically in delivery contexts.

Build and own production systems, not prototypes, and debug real-world issues in production.

Thrive in ambiguity by turning fuzzy problems into scoped, shipped systems without waiting to be asked.

Communicate clearly with non-technical stakeholders without dumbing down the message.

Have experience working in or alongside platform or infrastructure teams.

Demonstrate customer-facing technical work and the ability to build trust with engineering teams outside your own.

Show demonstrable, hands-on experience designing, building, and shipping production AI applications; experience limited to using tools such as ChatGPT, Claude, Cursor, or GitHub Copilot without building AI-powered products will not meet the requirements.

Practical notes

This role requires a degree as stated in the listing.

You must have 5+ years of software development experience as a must-have.

This role involves hybrid work with flexibility around schedules.

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Roles in platform engineering focus on enabling product teams through infrastructure and tooling.

Event-driven architecture supports responsive and resilient systems in cloud-native environments.

Modern enterprise software combines cloud-native platforms with AI and agentic capabilities.

Production AI engineering involves designing, building, and operating intelligent systems at scale.

Strong communication skills bridge technical teams and business stakeholders.

Continuous iteration based on real usage improves reliability and adoption.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.