Description We are looking for highly skilled and experienced Senior MLOps Engineer to join our team. This role will be involved in designing, implementing, and maintaining robust and scalable machine learning pipelines. This person will possess a strong background in DevOps practices, machine learning principles, and cloud computing platforms. You will work closely with data scientists and software engineers to … tune ML models for performance and accuracy. Understanding of statistical analysis and experimental design. Proficiency in data visualization and interpretation of ML results. Job responsibilities Proven experience as an MLOps Engineer or in a similar role, with an excellent understanding of AI/ML lifecycle management. Strong experience deploying and productionising ML models. Familiarity with data engineering concepts, including data … pipelines, ETL processes, and big data technologies. Excellent problem-solving skills and the ability to troubleshoot complex issues in AI/ML systems. Technical Insight Skills with MLOps concepts and principles. Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and containerization tools (e.g., Docker, Kubernetes). Proficiency in programming languages such as Python, experience with AI/ML frameworks More ❯
We are looking for a highly skilled and experienced Senior MLOps Engineer to join our team. This role will be involved in designing, implementing, and maintaining robust and scalable machine learning pipelines. This person will possess a strong background in DevOps practices, machine learning principles, and cloud computing platforms. You will work closely with data scientists and software engineers to … tune ML models for performance and accuracy. • Understanding of statistical analysis and experimental design. • Proficiency in data visualization and interpretation of ML results. Job Responsibilities • Proven experience as an MLOps Engineer or in a similar role, with an excellent understanding of AI/ML lifecycle management. • Strong experience deploying and productionizing ML models. • Familiarity with data engineering concepts, including data … pipelines, ETL processes, and big data technologies. • Excellent problem-solving skills and the ability to troubleshoot complex issues in AI/ML systems. Technical Insight • Skills with MLOps concepts and principles. • Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and containerization tools (e.g., Docker, Kubernetes). • Proficiency in programming languages such as Python, experience with AI/ML frameworks More ❯
Azure , GCP ). Experience deploying ML models or managing AI/ML workflows in production. Working knowledge of big data technologies like Spark , Hive , or Hadoop . Familiarity with MLOps tools (e.g., MLflow , Kubeflow , DataRobot ). Education Bachelor’s degree in Computer Science , Software Engineering , or a related technical field — or equivalent practical experience. Why Join Us Work on cutting More ❯
Azure , GCP ). Experience deploying ML models or managing AI/ML workflows in production. Working knowledge of big data technologies like Spark , Hive , or Hadoop . Familiarity with MLOps tools (e.g., MLflow , Kubeflow , DataRobot ). Education Bachelor’s degree in Computer Science , Software Engineering , or a related technical field — or equivalent practical experience. Why Join Us Work on cutting More ❯
and observability-either independently or in collaboration with other specialists. Optimize model pipelines for latency, scalability, and cost-efficiency , and support real-time and batch inference needs. Collaborate with MLOps, DevOps, and data engineering teams to ensure reliable model deployment and system integration. Stay informed on current research and emerging tools in LLMs, generative AI, and autonomous agents , and evaluate More ❯
in Python Excellent communication skills Preferred Qualifications & Skills: If you have the following characteristics, it would be a plus: Master's or PhD in Computer Science Some experience with MLOps would be very beneficial. Experience with building search applications using Azure Search, Sinequa, Elastic or anything Lucene-based would come in handy but is not necessary. Familiarity with Azure cloud More ❯
as clustering, classification, regression, and anomaly detection to discover patterns and trends in large datasets. Analyze and preprocess large datasets to extract meaningful insights and features for model training MLOps - Deployment into production environments, Monitoring and Maintenance Experience deploying and maintaining large-scale ML inference pipelines into production Implement and monitor model performance in production environments on Kubernetes and AWS … Proficiency in Python and SQL. Experience with big data technologies like Apache Hadoop and Apache Spark. Familiarity with real-time data processing frameworks such as Apache Kafka or Flink. MLOps & Deployment: Experience deploying and maintaining large-scale ML inference pipelines into production environments. Proficiency with Docker for containerization and Kubernetes for orchestration. Familiarity with AWS cloud platform (experience with GCP More ❯
measurement. Collaborative Development: Work closely with marketing strategists, data analysts, data engineers, and product owners to define use cases and deliver scalable solutions. Model Deployment & Monitoring: Deploy models using MLOps practices and tools (e.g., MLflow, Airflow, Docker, cloud platforms) ensuring performance, reliability, and governance compliance. Innovation & Research: Stay current on advancements in AI/ML and proactively bring forward new More ❯
measurement. Collaborative Development: Work closely with marketing strategists, data analysts, data engineers, and product owners to define use cases and deliver scalable solutions. Model Deployment & Monitoring: Deploy models using MLOps practices and tools (e.g., MLflow, Airflow, Docker, cloud platforms) ensuring performance, reliability, and governance compliance. Innovation & Research: Stay current on advancements in AI/ML and proactively bring forward new More ❯
release cycles Strong understanding of data science, machine learning concepts, and analytics capabilities Experience with AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, scikit-learn, etc.) Experience with MLOps practices (e.g., model deployment, monitoring, retraining workflows) for operationalizing machine learning models effectively Excellent communication skills with the ability to translate complex technical concepts for different audiences Proven track record More ❯
release cycles Strong understanding of data science, machine learning concepts, and analytics capabilities Experience with AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, scikit-learn, etc.) Experience with MLOps practices (e.g., model deployment, monitoring, retraining workflows) for operationalizing machine learning models effectively Excellent communication skills with the ability to translate complex technical concepts for different audiences Proven track record More ❯
City of London, London, United Kingdom Hybrid / WFH Options
un:hurd music
ML deployment, you will have the responsibility of managing projects with autonomy across the full data lifecycle. Responsibilities📝 As an ML Engineer at Un:hurd music, you'll be: MLOps and Model Deployment : Own the deployment, maintenance, and retraining of machine learning models in production environments, designing scalable system architectures to ensure performance and resilience. Data Collection and Integration : Develop More ❯
ML deployment, you will have the responsibility of managing projects with autonomy across the full data lifecycle. Responsibilities📝 As an ML Engineer at Un:hurd music, you'll be: MLOps and Model Deployment : Own the deployment, maintenance, and retraining of machine learning models in production environments, designing scalable system architectures to ensure performance and resilience. Data Collection and Integration : Develop More ❯
South East London, England, United Kingdom Hybrid / WFH Options
un:hurd music
ML deployment, you will have the responsibility of managing projects with autonomy across the full data lifecycle. Responsibilities As an ML Engineer at Un:hurd music, you'll be: MLOps and Model Deployment : Own the deployment, maintenance, and retraining of machine learning models in production environments, designing scalable system architectures to ensure performance and resilience. Data Collection and Integration : Develop More ❯
Job Description - Senior Manager - Machine Learning Engineering (006207) Our purpose is to make great financial decision making a breeze for everyone, and that purpose drives us every day. It's why we're on a mission to create an automated More ❯
production. We work closely with stakeholders across the business to expand the understanding and impact of machine learning and AI throughout Trainline. The Role We are looking for a MLOps Engineering Manager to join our team and help shape the future of train travel. You will be part of a highly innovative AI and ML team working alongside engineers, scientists … have the opportunity to work with fellow ML enthusiasts on large-scale production systems, delivering highly impactful products that make a difference to our millions of users. As a MLOps Engineering Manager at Trainline you will Build a new team of MLOps Engineers working alongside ML Engineers, Data Engineers, Software Engineers, Data Scientists and Product Managers Define MLOps processes and … modelling and evaluation techniques. Have experience with Cloud infrastructure (ideally AWS), DevOps technologies such as Docker or Terraform and CI/CD processes and tools. Have previously worked with MLOps tools like MLFlow and Airflow, or on common problems such as model and API monitoring, data drift and validation, autoscaling, access permissions Have previously worked with monitoring tools such as More ❯
Press Tab to Move to Skip to Content Link Job Title: Principal MLOps/GenAI Infrastructure Engineer Salary: £76,700 - 87,600 (The expected salary range for this role reflects internal benchmarking and external market insights.) Band: D Location: London/Salford/Glasgow/Newcastle/Cardiff (Candidates will be considered for either location. This is a hybrid role … and do the best work of their careers. Your Key Responsibilities And Impact Lead the design, development, and evolution of robust tooling and platforms to support scalable Data Science, MLOps, and LLMOps workflows across the organisation. Drive strategy and execution for deploying, serving, and monitoring large language models (LLMs) in real-time and batch environments using Amazon SageMaker, Bedrock, and … culture through mentorship, knowledge sharing, and thought leadership via deep dives, brown bags, internal tech talks, and cross-team collaboration. Your Skills And Experience Extensive experience in DevOps/MLOps roles with demonstrated impact in building, scaling, and securing ML/AI infrastructure in cloud-native environments. Strong experience with AWS services such as SageMaker, Bedrock, S3, EC2, Lambda, IAM More ❯
the platform, making the best use of available infrastructure Adapting existing solutions to use our inference service, ensuring a seamless transition What You Bring 5+ years working in an MLOps or related ML Engineering role Production experience self-hosting & operating LLMs at scale for generative tasks via an inference framework such as Ray or KServe (or similar) Production experience with More ❯
City Of London, England, United Kingdom Hybrid / WFH Options
CipherTek Recruitment
We are partnering with a prestigious investment bank to find a highly skilled and Hands-on Machine Learning Operations (MLOps) Lead. This role will be pivotal in building out a greenfield framework for the deployment and management of scalable AI/ML solutions, specifically for the front and Middle Office user base. The role is to define and set up … a greenfield standardized MLOps framework for capital markets and set up all the tools and best practices to educate data scientists and equip them with the right tools and expertise. You MUST be hands on. A strong understanding of Devops, Machine learning and Data engineering is required to enable to right candidate to implement the MLOps processes. This team are … areas and business stakeholders, so relationship building and good communication will be key. You will bring a expertise in data science or data engineering, with a specific focus on MLOps for at least 2 years . This platform is critical and will be rolled out across the bank, so we are looking for only the highest calibre candidates with experience More ❯
You will collaborate with a team of experts to solve complex business challenges using cutting-edge AI technologies. KEY RESPONSIBILITIES Design and architect scalable ML solutions using statistical modeling, MLOps frameworks and cloud technologies Build machine learning models for marketing attribution, customer prediction, segmentation, and recommendation systems with deep understanding of customer experience and marketing use-cases Implement end-to … SQL skills and experience with data warehousing solutions (Snowflake, BigQuery, Redshift) Experience with cloud platforms (AWS, Azure, GCP) and their ML services (SageMaker, Azure ML, Vertex AI) Knowledge of MLOps tools including Docker, Kubernetes, MLflow, Kubeflow, or similar platforms Experience with version control (Git) and collaborative development practices Excellent analytical thinking and problem-solving abilities Strong communication skills with ability More ❯
cleaning and ML validation strategies. Scalable Solutions: Building training pipelines and components to ensure scalable ML solutions, address errors, and provide education to upskill teams working on ML, enhancing MLOps proficiency. We offer a flexible hybrid working model with 2 days from home and 2-3 days at our central London office, where you'll collaborate with digital specialists and More ❯
About the Role We are seeking a Senior MLOps Engineer to join our AI team and drive the design, deployment, and continuous improvement of our MLOps platform. In this role, you will have the autonomy to shape our infrastructure, ensuring scalable, efficient, and robust operations that support rapid model iteration and deployment at scale. Key Responsibilities: Design & Implement ML Workflows … Build and Scale Our MLOps Platform Design, develop, and optimise a high-throughput model inference system for production-grade AI. Create streamlined workflow pipelines for model training, testing, monitoring and deployment, leveraging cloud-native tools. Cloud & Container Management: Deploy and manage applications using AWS services, Kubernetes, Docker, and other DevOps best practices. LLM Deployment & Fine Tuning: Drive the deployment and … Collaborate Across Teams: Work closely with Machine Learning engineers to enable their delivery What We're Looking For: Our ideal candidate will have a strong background in machine learning, MLOps, and cloud technologies with hands-on experience across the following areas: Cloud & DevOps Expertise: Experience with AWS, Kubernetes, and Docker. Proven skills in designing and managing CI/CD pipelines More ❯
attribution, predicting customer propensity, segmenting customer groups based on behavioral data, and recommending products and/or content. Fine-tune Large Language Models to business-specific use cases. Deploy MLOps pipelines together with our ML engineers. Work closely with data engineers, fellow data scientists/AI engineers, and consultants to ensure seamless integration and deployment of AI solutions that create … Excellent communication and problem-solving skills. Available to start working in the UK. Nice to Have Experience with natural language processing (NLP) or computer vision (CV). Knowledge of MLOps and model deployment pipelines. Familiarity with AI ethics and responsible AI principles. Experience working with large datasets and distributed computing. Contributions to open-source AI projects. We Offer Benefits vary More ❯
attribution, predicting customer propensity, segmenting customer groups based on behavioural data, and recommending products and/or content. Fine-tune Large Language Models to business-specific use cases. Deploy MLOps pipelines together with our ML engineers. Work closely with data engineers, fellow data scientists/AI engineers and consultants to ensure seamless integration and deployment of AI solutions that create … communication and problem-solving skills. Direct availability to start working in the UK. Nice to have: Experience with natural language processing (NLP) or computer vision (CV). Knowledge of MLOps and model deployment pipelines. Familiarity with AI ethics and responsible AI principles. Experience working with large datasets and distributed computing. Contributions to open-source AI projects. WE OFFER: The benefits More ❯
