AI Platform Engineer (DevOps / MLOps Focus)
Posted on May 7, 2026 by CV-Library
London, United Kingdom
IT
Immediate Start
£80k - £80k Annual
Full-Time
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.
49914MSR2
INDLON
The Portfolio Group are acting on behalf of our client in recruiting for this position
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.
49914MSR2
INDLON
The Portfolio Group are acting on behalf of our client in recruiting for this position
Reference: 225078399
https://jobs.careeraddict.com/post/113192792
AI Platform Engineer (DevOps / MLOps Focus)
Posted on May 7, 2026 by CV-Library
London, United Kingdom
IT
Immediate Start
£80k - £80k Annual
Full-Time
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.
49914MSR2
INDLON
The Portfolio Group are acting on behalf of our client in recruiting for this position
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.
49914MSR2
INDLON
The Portfolio Group are acting on behalf of our client in recruiting for this position
Reference: 225078399
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