AI Platform Engineer (DevOps / MLOps Focus)

We're hiring an experienced AI Platform Engineer to design, build and operate a production-grade Generative AI platform powering next-generation intelligent products. This is a hands-on engineering role focused on taking AI solutions from prototype to scalable, reliable services used in real-world environments.

You'll sit at the intersection of DevOps, cloud infrastructure and applied AI owning the full lifecycle of Retrieval-Augmented Generation (RAG) and LLM-powered systems across modern cloud architecture.

This role is about engineering, not research. You will architect and run the infrastructure that enables AI to perform securely, reliably and at scale ensuring performance, cost control and operational maturity as adoption grows.

You'll work closely with AI engineers, security teams, and product stakeholders to transform experimental models into hardened, production-ready services while shaping a reusable AI platform capable of supporting multiple products.

What You'll Be Doing

Design and optimise scalable RAG pipelines and vector search systems

Orchestrate multi-model AI services with a focus on latency, resilience and performance

Productionise GenAI workflows and ensure they operate reliably under real usage

Build and run AI services across AWS and Databricks

Develop ingestion, embedding and retrieval pipelines

Deploy containerised workloads via Kubernetes and Helm

Implement Infrastructure-as-Code using Terraform

Introduce end-to-end monitoring, tracing and alerting for AI workloads

Improve inference and retrieval performance while reducing operational cost

Establish fault-tolerant, scalable infrastructure patterns

Embed security, evaluation and governance into the AI lifecycle

Build CI/CD pipelines and automation to support continuous model deployment

Create reusable platform components to accelerate future AI initiatives

Strong experience in

Cloud infrastructure engineering (AWS-focused environments)

Kubernetes, containerisation, and distributed systems

Terraform / Infrastructure-as-Code

CI/CD, automation, and platform reliability

Running production workloads with high availability requirements

Plus, experience with one or more of the following

MLOps or ML platform engineering

RAG architectures, embeddings, or vector search

Model serving, observability or performance optimisation

Data / AI workflow orchestration in Databricks or similar ecosystems

Why Join?

Work on real-world AI systems operating at scale

Own platform design decisions and influence long-term architecture

Blend modern DevOps practices with cutting-edge Generative AI use cases

Be part of a growing, innovation-driven engineering environment

Opportunity to define how AI is operationalised across multiple products

If you're excited by building the infrastructure that makes AI usable, scalable and reliable in production, we'd love to hear from you.

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INDLON

The Portfolio Group are acting on behalf of our client in recruiting for this position.