15 of 15 MLflow Jobs in the South East

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, etc.). More ❯

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 ideas, frameworks, and techniques that … results for non-technical stakeholders. Strong proficiency in Python, SQL, and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch). Experience with model operationalization using tools like Docker, Kubernetes, MLflow, or SageMaker. Marketing KPIs knowledge: CTR, conversion rate, MQL to SQL, ROI, CLV, CAC, retention. Experience working with multi-channel marketing data: CRM (e.g., Salesforce), email, web analytics, social media More ❯

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 mindset and ability to work in a fast-paced, small team More ❯

as TensorFlow, PyTorch, Scikit-learn, and Keras. Understanding of algorithms and techniques for supervised and unsupervised learning. Experience with tools for model monitoring, logging, and performance evaluation, such as MLflow or Prometheus. Strong scripting skills in Bash, PowerShell, or similar scripting languages for automation of tasks and ability to write reusable and maintainable code to streamline ML operations Proficient in More ❯

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/Azure DevOps Jira, Confluence Bonus points for: MSc/PhD in ML or AI Databricks ML Engineer (Professional) certified More ❯

Refinitiv, prime brokerage APIs, and regulatory systems Advanced Security: Zero-trust architecture, FCA compliance, and financial-grade security protocols Technical Stack You'll Lead AI/ML: TensorFlow, PyTorch, MLflow, NVIDIA Triton, custom financial LLMs Data: Kafka, Flink, Snowflake, ClickHouse, Neo4j, vector databases Backend: FastAPI, GraphQL, microservices, Docker, Kubernetes Frontend: React/Next.js, TypeScript, real-time dashboards, mobile apps Cloud More ❯

FastAPI, or other common web frameworks. An understanding of core concepts in ML, data science and MLOps. Nice-to-Have : Built agentic workflows/LLM tool-use. Experience with MLFlow, WandB, LangFuse, or other MLOps tools. Experience with AirFlow, Spark, Kafka or similar. Why Plexe? Hard problems: we're automating the entire ML/AI lifecycle from data engineering to More ❯

ability to translate complex analyses into actionable insights. Nice-to-Haves: Familiarity with marketing-specific measurement models such as Media Mix Modelling (MMM). Knowledge of model versioning (e.g. MLFlow), API frameworks (FastAPI), or building dashboards (e.g. Dash or Streamlit). The Opportunity: You’ll work across multiple industries and household-name brands, contributing to meaningful campaigns powered by cutting More ❯

to the delivery of complex business cloud solutions. The ideal candidate will have a strong background in Machine Learning engineering and an expert in operationalising models in the Databricks MLFlow environment (chosen MLOps Platform). Responsibilities: Collaborate with Data Scientists and operationalise the model with auditing enabled, ensure the run can be reproduced if needed. Implement Databricks best practices in More ❯

occasions. To be a successful, the ideal Machine Learning Engineer candidate will have: · Highly skilled in Python. · Knowledge of AWS or GCP. · Ideally experience of SKLearn/Docker/MLFlow or PyTest · Excellent communication and problem solving skills. What is in it for you? As a talented Machine Learning Engineer you can expect: · Great salary - Up to £80,000 base More ❯

occasions. To be a successful, the ideal Machine Learning Engineer candidate will have: Highly skilled in Python. Knowledge of AWS or GCP. Ideally experience of SKLearn/Docker/MLFlow or PyTest Excellent communication and problem solving skills. What is in it for you? As a talented Machine Learning Engineer you can expect: Great salary - Up to £80,000 base More ❯

control, unit testing, CI/CD pipelines, model monitoring, and reproducibility. We use Python, SQL, Unix-based systems, git, and github for collaboration and review, including Hugging Face, Hydra, MLFlow, DVC, etc., for experiment tracking and monitoring. Experience working with the OMOP Common Data Model and working with Standard Medical Vocabularies, such as SNOMED, ICD10. Knowledge of other clinical data More ❯

interactive AI demos and proofs-of-concept with Streamlit, Gradio, or Next.js for stakeholder feedback; MLOps & Deployment: Implement CI/CD pipelines (e.g., GitLab Actions, Apache Airflow), experiment tracking (MLflow), and model monitoring for reliable production workflows; Cross-Functional Collaboration: Participate in code reviews, architectural discussions, and sprint planning to deliver features end-to-end. Requirements: Master’s degree in More ❯

BI Tools: Tableau, Metabase, Google Data Studio DevOps Familiarity (Plus): Kubernetes, CI/CD, infrastructure monitoring Web3 Ecosystem Tools: Etherscan, Tenderly, The Graph, Cosmos Hub AI Ops Stack (Plus): MLflow, Weights & Biases, Ray Serve monitoring What We Offer Competitive salary + equity/token package Flexible, remote-first work environment High-impact leadership role in a fast-scaling frontier tech More ❯

work collaboratively across disciplines. ● Experience integrating or leveraging Large Language Models (LLMs) via APIs or platform services (e.g., OpenAI, Anthropic, Cohere). ● Familiarity with MLOps tools and practices (e.g., MLflow, SageMaker, Kubeflow). ● Experience in startup or fast-paced environments with rapidly evolving requirements Personal qualities ● Ambitious ● Driven ● Ability to work as part of a collaborative team but also work More ❯