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.

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• 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:

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• 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:

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• 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
  

• 
  
• 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.

49914MS

INDLON

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