AI / Data / MLOps Engineer

• Design, build and productionise AI/ML and Generative AI services, including LLM, RAG and agent-based solutions.
• Develop reusable AI platform components including API wrappers, orchestration templates, guardrails and integration patterns.
• Build RAG pipelines, knowledge retrieval workflows, vector embeddings, chunking strategies and knowledge graph integrations.
• Develop and maintain data ingestion and integration pipelines from structured and unstructured enterprise data sources.
• Build integrations using REST APIs, webhooks, event streams and event-driven architectures.
• Implement scalable AWS ingestion patterns using services such as API Gateway, Lambda, EventBridge, SQS and DynamoDB.
• Build and populate knowledge graphs programmatically, including entity/relationship extraction, loading, validation and data quality management.
• Implement data migration and backfill activities when onboarding new data sources.
• Build and maintain secure cloud-native infrastructure using Infrastructure as Code (Terraform/Ansible).
• Implement and improve CI/CD, deployment pipelines, environment management, testing and release processesfor AI/ML workloads.
• Establish appropriate logging, monitoring, telemetry, alerting, cost tracking and operational controls.
• Implement state/watermark tracking and reconciliation processes to identify ingestion or data delivery gaps.
• Apply enterprise security, IAM, networking, governance and environment-isolation standards.
• Implement prompt strategies, AI guardrails and responsible AI controls.
• Conduct structured evaluation of RAG/LLM solutions, including retrieval quality, hallucination mitigation and iterative improvement.
• Work with delivery teams and SMEs to integrate AI capabilities into existing applications and engineering workflows.
• Identify and improve DevOps/SDLC processes that may prevent effective adoption of AI-assisted development and agentic workflows.
• Develop reusable engineering patterns and contribute improvements back to the wider AI platform.

Only Apply if you are leaving in UK & have right to Work in UK

We are looking for an experienced AI / Data / MLOps Engineer to support the development of an enterprise-grade AI platform, enabling secure, scalable and production-ready use of Generative AI, Machine Learning and enterprise data.

This is a hands-on engineering role covering AI engineering, data integration, knowledge engineering, cloud infrastructure and MLOps/DevOps. The role will focus on translating architectural designs into production-ready services and reusable platform capabilities that integrate with enterprise applications, data sources, workflows and SDLC processes., * Strong Python development experience, particularly for AI, data and integration engineering.

• Strong backend engineering and API development/integration experience.
• Hands-on experience with Generative AI, LLM APIs and production AI solutions.
• Experience implementing RAG, vector search, embeddings and/or agent-based systems.
• Strong data engineering experience, including ingestion, ETL/ELT and integration pipelines.
• Experience with REST APIs, webhooks, messaging and event-driven architectures.
• Strong AWS and/or Azure cloud engineering experience.
• Experience with cloud services such as AWS Lambda, API Gateway, EventBridge, SQS, DynamoDB, Bedrock and OpenSearch, or equivalent Azure services.
• Experience with Infrastructure as Code, particularly Terraform and/or Ansible.
• Strong DevOps/MLOps and CI/CD experience.
• Experience with Git-based development, branching, release and environment management.
• Understanding of AI/ML productionisation, observability, evaluation and operational support.
• Good understanding of enterprise security, IAM, networking, data governance and responsible AI principles.
• Experience building scalable APIs and distributed systems.

Desirable Skills

• Knowledge graph technologies such as Neo4j/Cypher or RDF/SPARQL.
• Experience with AWS Bedrock and OpenSearch.
• Experience with orchestration frameworks such as LangGraph or equivalent.
• Experience with Apache Airflow and/or dbt.
• Experience with AWS data migration technologies such as AWS DMS.
• Experience integrating with enterprise tools such as JIRA, Confluence, GitLab or similar platforms.
• Experience with AI-assisted software development tools and agentic engineering workflows.
• Understanding of model/retrieval evaluation, hallucination mitigation and AI guardrails.
• Experience working with data lineage, quality, reconciliation and monitoring.
• Experience working within regulated, enterprise or government environments.

Ideal Candidate

The ideal candidate will have a strong engineering background with experience across AI/ML, data engineering, cloud and DevOps/MLOps. They do not need to be a specialist in every technology listed but should have strong hands-on expertise in several of these areas and be comfortable working across the wider AI platform lifecycle.