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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI / Data / MLOps Engineer - **Company:** SS Technologies - **Location:** UK (Remote available) - **Salary:** £78,000.0 - **Contract:** Temporary contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Confluence, JIRA, Microsoft Azure, Cloud Computing, Continuous Integration, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Migration, DevOps, Distributed Systems, Amazon DynamoDB, Graph Database, Identity and Access Management, Information Retrieval, Python (Programming Language), Machine Learning, Neo4j, Systems Development Life Cycle, Cloud Services, Ansible, Search Technologies, SPARQL, Systems Integration, Enterprise Data Management, Data Logging, Enterprise Software Applications, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Generative AI, AWS Lambda, Gitlab, Git, Servicebus, Event Driven Architecture, AI Platforms, Kubernetes, Data Lineage, Machine Learning Operations, Data Delivery, Api Design, Api Gateway, Restful APIs, Amazon Simple Queue Service (SQS), Terraform, Webhooks, Data Pipelines - **Published:** September 29, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=8f0ba05120cdf024 ## About the Role 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. ## Description * 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. ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [GitOps for the people](https://www.wearedevelopers.com/videos/461-gitops-for-the-people) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [How E.On productionizes its AI model & Implementation of Secure Generative AI.](https://www.wearedevelopers.com/videos/623-how-e-on-productionizes-its-ai-model-implementation-of-secure-generative-ai) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts)