1 to 25 of 93 MLOps Jobs in the South East

of 5years. Strong programming skills in Python and preferably SQL, javascript. Experience with Agentic AI. Familiarity with cloud platforms (AWS, Azure, Google Cloud) and MLOps tools. Understanding of data structures, algorithms, and software engineering principles and also when to prototype. Experience with data preprocessing, feature engineering, and model evaluation techniques. More ❯

Mathematics, or a technical field. Proficiency in Python (or R), with hands-on experience in ML and statistical modeling. End-to-end experience with MLOps , from experimentation to deployment and monitoring. Strong grasp of ethical AI practices: model explainability, transparency, bias mitigation. Hands-on with AWS services (SageMaker, Glue, Redshift More ❯

our platform. Ensure smooth end-to-end integration of AI/ML functionality within broader product development efforts. Technical Leadership Define best practices for MLOps, model deployment, and performance tuning. Evaluate and implement new frameworks, tools, and cloud-native AI solutions (including LLMs). Make strategic technical decisions that align More ❯

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 More ❯

techniques. Proficient in version control, CI/CD pipelines, and test-driven development. Nice to Have: Experience within financial services or fintech. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, SageMaker). Understanding of regulatory or compliance frameworks. Academic background in mathematics, statistics, or quantitative disciplines. Who You Are: A natural More ❯

and maintaining real-time data pipelines. Experience with ETL processes, Python, and SQL. Familiarity with big data technologies like Apache Hadoop and Apache Spark. MLOps & Deployment: Experience deploying and maintaining ML inference pipelines. Proficiency with Docker and Kubernetes. Familiarity with AWS cloud platform. The Perfect Candidate Also Has : Programming Languages More ❯

ability to engage senior stakeholders Nice to Have: Experience in consulting or client-facing delivery roles Knowledge of cloud platforms (AWS, GCP, Azure) and MLOps best practices Advanced degree (MSc or PhD) in a quantitative discipline Why Join Us? High-impact work with world-class clients Competitive salary and bonus More ❯

other relevant programming languages. Experience with ML frameworks like TensorFlow, PyTorch, and Scikit-learn. Hands-on experience with Microsoft Azure AI services. Knowledge of MLOps best practices. Experience with Azure data and analytics services such as Data Factory, Data Lake, and Synapse. Experience designing AI solutions, including virtual assistants and More ❯

CI/CD for microservices or ML pipelines. Solid understanding of networking, Linux, and security fundamentals. Preferred Qualifications Experience supporting GPU‑accelerated workloads or MLOps/LLMOps platforms. Familiarity with service mesh (Istio, Linkerd) and event‑driven architectures (Kafka, Pub/Sub). Knowledge of distributed storage systems (Ceph, MinIO More ❯

PyTorch, Hugging Face Transformers, Pandas, and scikit-learn. Strong understanding of cloud services (preferably AWS) and experience with Docker, Kubernetes, MLFlow, Kubeflow, or similar MLOps tools. 📩 Interested? Apply below or email me at mmatysik@trg-uk.com. More ❯

sources, 3rd party providers and publicly available data. Technology & Infrastructure Advisory: Assess and recommend data science tooling, platform architecture (cloud/on‑premise), and MLOps practices to ensure scalable, maintainable analytics pipelines (including API calls to various services) What You’ll Bring: Extensive experience in data strategy and analytics consulting More ❯

Scala, C# or Java, cloud SDKs and APIs. AI/ML expertise for pipeline efficiency, familiar with TensorFlow, PyTorch, AutoML, Python/R, and MLOps (MLflow, Kubeflow). Solid in DevOps, CI/CD automation with Bitbucket Pipelines, Azure DevOps, GitHub. Automate deployment of data pipelines and applications using Bash More ❯

sensitive data (HIPAA/GDPR compliance awareness). Strong problem-solving skills and ability to move fast in a startup environment. Bonus: Experience with MLOps, Kubernetes, AWS/GCP, and deploying models in production. Work Permit UK work permit required More ❯

enterprise integration. Expertise in LLMs, NLP/NLU, Conversational/GenAI, AI Search, and Virtual Agents. Proficiency in programming & AI development (Python, OpenAI APIs, MLOps frameworks). Nice-to-Haves: Experience with multilingual AI models for global translation. AI certifications (e.g., Azure AI Engineer, Google ML Engineer, TOGAF). What More ❯

enterprise integration. Expertise in LLMs, NLP/NLU, Conversational/GenAI, AI Search, and Virtual Agents. Proficiency in programming & AI development (Python, OpenAI APIs, MLOps frameworks). Nice-to-Haves: Experience with multilingual AI models for global translation. AI certifications (e.g., Azure AI Engineer, Google ML Engineer, TOGAF). What More ❯

for AI model deployment. Data Visualisation: Ability to present insights effectively using tools like Matplotlib, Seaborn, Tableau, or Power BI. Model Deployment: Understanding of MLOps principles, APIs, and containerization (Docker, Kubernetes) for productionizing AI models. Business-Focused AI Application: Ability to translate AI solutions into measurable business impact. Why Wowcher More ❯

happen. Familiarity with ML experiment tracking and collaboration tools, such as Mlflow and Weights & Biases. A solid background in software engineering and DevOps practices, MLOps deployment, and infrastructure. A knack for generative AI frameworks and SDKs, like RAG, Langchain/LangGraph, Semantic Kernel, and tools such as MS tooling, Co More ❯

development ✔ Strong Python, SQL, and Databricks skills (especially PySpark & Delta Lake) ✔ Deep experience with Azure: Data Factory, Synapse, Event Hub, Azure Functions ✔ Understanding of MLOps tooling like MLflow and integration with AI pipelines ✔ Exposure to data for LLM workflows, unstructured sources, and real-time ingestion ✔ Comfortable operating in an early More ❯

processes Familiarity with cloud platforms, particularly Microsoft Azure Understanding of microservices architecture and distributed systems Experience with DevOps practices for AI/ML workflows (MLOps) Strong programming skills in Python and related data science libraries Demonstrated ability to work within solution architecture frameworks Other preferred skills Experience with multiple cloud More ❯

Kafka. Strong understanding of SQL, NoSQL, and data modeling. Familiarity with cloud platforms (AWS, Azure, GCP) for deploying ML and data solutions. Knowledge of MLOps practices and tools, such as MLflow or Kubeflow. Strong problem-solving skills, with the ability to troubleshoot both ML models and data systems. A collaborative More ❯

in the field. You'll join a global team with a distributed set of skills including Research, Applied AI and Engineering. They are seeking MLOps Engineers to help architect the future of communication through AI. This isn't just another engineering role – it's an opportunity to pioneer systems that … transform how companies connect with their customers What You’ll Be Doing You'll be designing and optimising production-grade MLOps pipelines that bring cutting-edge Generative AI and LLMs from experimentation to real-world impact. Your expertise will directly influence how some of the world's leading brands enhance … their strategies. What You'll Build Production-Ready GenAI Infrastructure : Design and deploy scalable MLOps pipelines specifically optimized for GenAI applications and large language models State-of-the-Art Model Deployment : Implement and fine-tune advanced models like GPT and similar architectures in production environments Hybrid AI Systems : Create solutions More ❯

systems Practical experience with LLMs and their applications Proficiency in Python and ML frameworks (TensorFlow, PyTorch, etc.) Knowledge of containerization, CI/CD, and MLOps practices More ❯

years in Data Science/ML Engineering 2+ years hands-on with Databricks Strong track record delivering production-grade ML models Solid grasp of MLOps best practices Confident speaking to technical and non-technical stakeholders 🛠️ Tech you’ll be using: Python, SQL, Spark, R MLflow, vector databases GitHub/GitLab More ❯

such as Scikit learn, Keras, Tensor flow, and PySpark. Experience and understanding of mixed technical teams such as engineering, architects, business analysts. Familiar with MLOps industry best practices. Understanding of the financial industry, in particular insurance, would be advantageous. If you are interested in this opportunity get in touch today More ❯

and client audiences Nice-to-Have: Experience with marketing data, customer-level modelling, or decision science (e.g. uplift, attribution, causal AI, optimization) Familiarity with MLOps tooling (MLflow, FastAPI, Airflow, etc.) Experience designing and interpreting A/B tests or other experimental frameworks Background in consulting, agency, or fast-paced environments More ❯