LLMs) and prompt engineering (e.g., GPT, BERT, T5 family). • Familiarity with on-device or edge-AI deployments (e.g., TensorFlow Lite, ONNX, mobile/embedded inference). • Knowledge of MLOps tooling (MLflow, Weights & Biases, Kubeflow, or similar) for experiment tracking and model governance. • Open-source contributions or published papers in top-tier AI/ML conferences (NeurIPS, ICML, CVPR, ACL More ❯
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 ❯
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 ❯
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 ❯
e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring, logging, CI/CD for ML, reproducibility. Experience with Docker and container orchestration (e.g., Kubernetes). Preferred Skills: Experience with feature stores (e.g., Feast, Tecton). Knowledge of … . Familiarity with reinforcement learning or generative AI models. Tools & Technologies: Languages: Python, SQL (optionally: Scala, Java for large-scale systems) ML Frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM MLOps: MLflow, Weights & Biases, Kubeflow, Seldon Core Data Processing: Pandas, NumPy, Apache Spark, Beam Model Serving: TensorFlow Serving, TorchServe, FastAPI, Flask Cloud Platforms: AWS (SageMaker, S3, EC2), Google Cloud AI Platform More ❯
Haywards Heath, Sussex, United Kingdom Hybrid / WFH Options
First Central Services
testing, validation, deployment, to monitoring and retraining of models within different environments. If you've a strong understanding of Microsoft Azure, fluency in data science coding, and expertise in MLOps frameworks, we want to hear from you. Bring your excellent communication, problem-solving, and organizational skills to our team and help us drive innovation and excellence. This is a flexible … Synapse, and Data Factory) Fluency in common data science coding capabilities such as Python and modelling frameworks such as Pytorch, Tensorflow etc You'll be skilled in application of MLOps frameworks within a production environment Excellent communication skills, both verbal and written Strong time management and organisation skills Ability to diagnose and troubleshoot problems quickly Excellent problem-solving and analytic … services, Event Hubs, Synapse, and Data Factory) Fluency in common data science coding capabilities such as Python and modelling frameworks such as Pytorch, Tensorflow etc. Skilled in application of MLOps frameworks within a production environment Excellent communication skills, both verbal and written Strong time management and organisation skills Ability to diagnose and troubleshoot problems quickly Excellent problem-solving and analytic More ❯
testing, validation, deployment, to monitoring and retraining of models within different environments. If you've a strong understanding of Microsoft Azure, fluency in data science coding, and expertise in MLOps frameworks, we want to hear from you. Bring your excellent communication, problem-solving, and organizational skills to our team and help us drive innovation and excellence. This is a flexible … Synapse, and Data Factory) Fluency in common data science coding capabilities such as Python and modelling frameworks such as Pytorch, Tensorflow etc You'll be skilled in application of MLOps frameworks within a production environment Excellent communication skills, both verbal and written Strong time management and organisation skills Ability to diagnose and troubleshoot problems quickly Excellent problem-solving and analytic … services, Event Hubs, Synapse, and Data Factory) Fluency in common data science coding capabilities such as Python and modelling frameworks such as Pytorch, Tensorflow etc. Skilled in application of MLOps frameworks within a production environment Excellent communication skills, both verbal and written Strong time management and organisation skills Ability to diagnose and troubleshoot problems quickly Excellent problem-solving and analytic 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 ❯
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 ❯
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 ❯
positive change and use AI for good. We are growing our team of engineers to help deliver an increasing number of client projects. You won't need an existing MLOps background to apply for this role. You'll learn as you go by immersing yourself in this exciting new field. What we're looking for We've seen considerable growth … over the past few years and this will continue as we build on our reputation as Open Source MLOps experts, while working with the community, and delivering solutions to our clients. The ideal candidate would be motivated to grow and progress in line with the company's ambitions. As well as being a great engineer, motivated by the chance to … be at the forefront of MLOps adoption, you'll also enjoy being part of a culture that values: Loving what we do: a real passion for Open Source, MLOps and taking pride in great work. Just trying it: MLOps is an exciting new field. We love to develop new skills, solve new problems and thrive on a challenge. Being greater 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 ❯
operations throughout the United States as well as in Belfast. For more information, visit DailyPay's Press Center. The Role: We are seeking a highly skilled and motivated Senior MLOps Engineer to join our growing team in Belfast. You will play a crucial role in maturing and scaling our machine learning infrastructure and processes, ensuring the reliability, scalability, and performance … of our ML models in production. This role requires a strong background in MLOps principles, cloud technologies (AWS), and a passion for building robust and efficient systems. You will collaborate closely with data scientists, engineers, and product teams to deliver high-quality ML solutions that directly impact our business. The mission of the Data Science Team is critical for the … development and success of our product, understanding the needs of our customers and partners, and maintaining DailyPay's leading role in the early wage access industry. As a staff MLOps Engineer, you will be instrumental in helping to realize this vision for DailyPay by enabling efficient and effective deployment and management of machine learning models. How You Will Make an More ❯
Press Tab to Move to Skip to Content Link • Job Title: Senior MLOps/GenAI Infrastructure Engineer • Location: London/Salford/Glasgow/Newcastle/Cardiff (This is a hybrid role and the successful candidate will balance office working with home working) • Band: D • Salary: up to £59,600 - £69,800 (The expected salary range for this role reflects … modern digital ecosystem using exciting technologies and do the best work of their careers. Your Key Responsibilities And Impact Designing, developing, and maintaining tools that support data science and MLOps/LLMOps workflows. Collaborate with Data Scientists to deploy, serve, and monitor LLMs in real-time and batch environments using Amazon SageMaker, Bedrock Implement Infrastructure-as-Code with AWS CDK … and knowledge-sharing through comprehensive documentation, technical deep dives, brown bag sessions, internal workshops, and active mentorship of team members. Your Skills And Experience Extensive experience of DevOps/MLOps experience with a strong focus on building and delivering scalable infrastructure for ML and AI applications using Python and cloud native technologies Experience with cloud services, especially Amazon Web Services 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 ❯
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 ❯
of git Experienced in deploying solutions such as ML models, API and dashboards Deploying data science solutions on a cloud platform; Azure ML and MSFT Certificate are highly desirable MLOps is highly desirable, e.g. CI/CD, Feature Store, drift monitoring, MLflow, DVC, Docker, Kubernetes Software development experience is desirable Algorithm design experience is desirable Data architecture knowledge is desirable 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 ❯
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 ❯
warehouse architecture and development, infrastructure as code (IaC) using Terraform , and data extraction from both structured and unstructured data sources (e.g. websites). Knowledge using the Microsoft Azure ecosystem, MLOps, Kubernetes , and other modern data engineering practices. Core Responsibilities Data Architecture & Development: Devesign and implement scalable, secure, and high-performance data lake and data warehouse solutions. Lerage best practices in … data solutions. Infrastructure as Code (IaC): Use Terraform to automate the provisioning and management of cloud infrastructure. Define reusable and modular Terraform configurations to support scalable deployment of resources. MLOps: Collaborate with data scientists and machine learning engineers to operationalise machine learning models. Implement CI/CD pipelines for machine learning workflows, ensuring efficient model deployment and monitoring. Containerisation and … track record of designing and implementing data lakes and warehouses (experience with Azure is a plus). Demonstrated experience with Terraform for infrastructure provisioning and management. Solid understanding of MLOps practices, including model training, deployment, and monitoring. Hands-on experience with Kubernetes and containerised environments. Technical Skills: Proficiency in programming languages such as Python & SQL. Experience with distributed computing frameworks More ❯
warehouse architecture and development, infrastructure as code (IaC) using Terraform , and data extraction from both structured and unstructured data sources (e.g. websites). Knowledge using the Microsoft Azure ecosystem, MLOps, Kubernetes , and other modern data engineering practices. Core Responsibilities Data Architecture & Development: Devesign and implement scalable, secure, and high-performance data lake and data warehouse solutions. Lerage best practices in … data solutions. Infrastructure as Code (IaC): Use Terraform to automate the provisioning and management of cloud infrastructure. Define reusable and modular Terraform configurations to support scalable deployment of resources. MLOps: Collaborate with data scientists and machine learning engineers to operationalise machine learning models. Implement CI/CD pipelines for machine learning workflows, ensuring efficient model deployment and monitoring. Containerisation and … track record of designing and implementing data lakes and warehouses (experience with Azure is a plus). Demonstrated experience with Terraform for infrastructure provisioning and management. Solid understanding of MLOps practices, including model training, deployment, and monitoring. Hands-on experience with Kubernetes and containerised environments. Technical Skills: Proficiency in programming languages such as Python & SQL. Experience with distributed computing frameworks More ❯
