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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Engineer - **Company:** Hays plc - **Location:** Manchester, UK - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Microsoft Azure, Big Data, Business Systems, Cloud Computing, Continuous Integration, Information Engineering, Distributed Data Store, Python (Programming Language), Machine Learning, NoSQL, NumPy, Performance Tuning, Tensorflow, Software Engineering, SQL Databases, Data Streaming, Management of Software Versions, Data Processing, Google Cloud, Enterprise Software Applications, Cloud Platform System, Feature Engineering, Data Ingestion, Pytorch, DevOps Tools - Open-source, Large Language Models, Apache Spark, Model Validation, Generative AI, Pandas, Scikit Learn, Kubernetes, Machine Learning Operations, Docker, Databricks, Microservices - **Published:** June 4, 2026 - **Apply:** https://www.hays.co.uk/job-detail/ml-engineer-manchester_4802431 ## About the Role * Strong commercial experience (typically 4-8+ years) in machine learning, data engineering, or software engineering * Proven experience deploying machine learning models into production environments * Strong hands-on experience with end-to-end ML pipelines (data ingestion * training * deployment * monitoring) * Experience implementing MLOps practices, including CI/CD pipelines and model life cycle management * Strong background in data processing and feature engineering * Experience working with large-scale datasets and distributed data systems * Experience integrating ML models into APIs, applications, and business systems * Solid understanding of model evaluation, optimisation, and performance tuning * Experience working in cloud environments (Azure, AWS, or GCP) * Proven ability to work in cross-functional agile teams * Previous contract or consulting experience in enterprise environments is highly desirable. Key Skills * Python (essential) * ML frameworks (Scikit-learn, TensorFlow, PyTorch) * Data processing tools (Pandas, NumPy) * SQL / NoSQL databases * Cloud platforms (Azure, AWS, GCP) * Docker, Kubernetes (desirable) * CI/CD and DevOps tooling Desirable Experience * Experience with real-time / streaming ML systems * Familiarity with Databricks, Spark, or big data platforms * Exposure to LLMs / Generative AI (RAG, embeddings, etc.) * Experience with feature stores and modern ML tooling (e.g., Feast) * Knowledge of AI governance and model explainability * Industry experience in [Finance / Retail / Healthcare - tailor as needed] ## Description Duration: Initial 6 months (with strong extension potential) About the Role We're working with a leading organisation seeking a skilled Machine Learning Engineer to support the development and deployment of scalable ML solutions. This is a hands-on contract role, ideal for someone who can take models from concept to production, working closely with data scientists, engineers, and stakeholders to deliver high-impact machine learning capabilities. Key Responsibilities * Design, build, and deploy production-grade machine learning models * Develop and maintain data pipelines and feature engineering workflows * Collaborate with data scientists to operationalise models and improve performance * Implement MLOps best practices, including CI/CD, monitoring, and versioning * Optimise models for scalability, reliability, and performance in production * Integrate ML solutions into APIs, microservices, and enterprise systems * Work with large datasets to ensure data quality, validation, and availability * Monitor models in production and implement retraining and performance tuning pipelines * Collaborate with cross-functional teams to translate business requirements into ML solutions Experience Required * Strong commercial experience (typically 4-8+ years) in machine learning, data engineering, or software engineering * Proven experience deploying machine learning models into production environments * Strong hands-on experience with end-to-end ML pipelines (data ingestion * training * deployment * monitoring) * Experience implementing MLOps practices, including CI/CD pipelines and model life cycle management * Strong background in data processing and feature engineering * Experience working with large-scale datasets and distributed data systems * Experience integrating ML models into APIs, applications, and business systems * Solid understanding of model evaluation, optimisation, and performance tuning * Experience working in cloud environments (Azure, AWS, or GCP) * Proven ability to work in cross-functional agile teams * Previous contract or consulting experience in enterprise environments is highly desirable. Key Skills * Python (essential) * ML frameworks (Scikit-learn, TensorFlow, PyTorch) * Data processing tools (Pandas, NumPy) * SQL / NoSQL databases * Cloud platforms (Azure, AWS, GCP) * Docker, Kubernetes (desirable) * CI/CD and DevOps tooling Desirable Experience * Experience with real-time / streaming ML systems * Familiarity with Databricks, Spark, or big data platforms * Exposure to LLMs / Generative AI (RAG, embeddings, etc.) * Experience with feature stores and modern ML tooling (e.g., Feast) * Knowledge of AI governance and model explainability * Industry experience in [Finance / Retail / Healthcare - tailor as needed] What's on Offer * Opportunity to work on high-impact machine learning projects * Collaborative and forward-thinking engineering environment * Flexible working arrangements * Competitive day rate with extension potential Apply Now If you're a skilled Machine Learning Engineer looking for your next contract and want to work on meaningful ML solutions, we'd love to hear from you. #4802431 - Ashley ## Related Videos - [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Vectorize all the things! 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