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 ❯
learn). Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Hands-on experience with CI/CD pipelines, version control, and ML workflows (MLflow, Kubeflow). Proven track record of delivering ML models that solve real-world business challenges at scale. Excellent communication skills with the ability to work effectively in cross-functional teams. More ❯
with our AI research team to streamline the transition of models from research to production within the Ultralytics HUB ecosystem. Managing our experiment tracking and versioning using tools like MLflow and DVC. Your work will be critical to ensuring that our state-of-the-art models are accessible, reliable, and performant for our global user base. 🛠️ Skills and Experience 5+ … tools such as Terraform or Ansible. Familiarity with GPU acceleration using CUDA and model optimization for inference. Knowledge of MLOps tools for experiment tracking, and model serving such as MLflow, Kubeflow, or Weights & Biases. Excellent problem-solving skills and the ability to perform in a fast-paced, high-intensity environment. 🌟 Cultural Fit - Intensity Required Ultralytics is a high-performance environment More ❯
City of London, London, United Kingdom Hybrid / WFH Options
Talent Hero Ltd
/B testing and statistical analysis to validate approaches Document ML systems and provide support for ongoing performance tuning Use tools like Python, TensorFlow, PyTorch, Scikit-learn, AWS, GCP, MLflow, Docker, SQL , and others Requirements Minimum Bachelors degree in Computer Science, Machine Learning, AI, or a related field Proven experience as a Machine Learning Engineer or in a similar role 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 ❯
mentoring and managing data science teams. Deep knowledge of media measurement techniques, such as media mix modelling. Experience with advanced AI techniques, including NLP, GenAI, and CausalAI. Familiarity with MLFlow, API design (FastAPI), and dashboard building (Dash). If this role looks of interest, reach out to Joseph Gregory More ❯
mentoring and managing data science teams. Deep knowledge of media measurement techniques, such as media mix modelling. Experience with advanced AI techniques, including NLP, GenAI, and CausalAI. Familiarity with MLFlow, API design (FastAPI), and dashboard building (Dash). If this role looks of interest, reach out to Joseph Gregory More ❯
City of London, London, United Kingdom Hybrid / WFH Options
Made Tech
Proven track record of leading complex AI projects in an agile environment Strong technical proficiency with modern ML tools and frameworks: PyTorch, TensorFlow, SparkMLLib, SciPy, Scikit-Learn, NLTK (etc) MLflow or similar ML lifecycle tools Cloud platforms (AWS/Azure/GCP) Experience with AI governance and responsible AI practices Understanding of public sector data requirements and compliance Outstanding communication More ❯
value from their data assets. 🔧 Tech you’ll be working with: • Azure Data Factory, Data Lake, Synapse, Databricks • SQL, Python, Spark, DAX • Azure DevOps, CI/CD, Git • Bonus: MLflow, Kubernetes, Kimball modelling, MLOps 🧠 What you’ll do: • Build & maintain modern cloud-based data pipelines • Translate business needs into scalable data solutions • Solve data quality issues and improve infrastructure • Collaborate More ❯
value from their data assets. 🔧 Tech you’ll be working with: • Azure Data Factory, Data Lake, Synapse, Databricks • SQL, Python, Spark, DAX • Azure DevOps, CI/CD, Git • Bonus: MLflow, Kubernetes, Kimball modelling, MLOps 🧠 What you’ll do: • Build & maintain modern cloud-based data pipelines • Translate business needs into scalable data solutions • Solve data quality issues and improve infrastructure • Collaborate More ❯
value from their data assets. 🔧 Tech you’ll be working with: • Azure Data Factory, Data Lake, Synapse, Databricks • SQL, Python, Spark, DAX • Azure DevOps, CI/CD, Git • Bonus: MLflow, Kubernetes, Kimball modelling, MLOps 🧠 What you’ll do: • Build & maintain modern cloud-based data pipelines • Translate business needs into scalable data solutions • Solve data quality issues and improve infrastructure • Collaborate More ❯
startup environments UK-based and available to work 2–3 days per week in-office (London) Bonus Points Experience in healthcare, medtech, or clinical systems Familiarity with MLOps tooling (MLflow, Kubeflow, Vertex Pipelines More ❯
and semantic similarity. Strong proficiency in Python, including use of ML libraries such as TensorFlow, PyTorch, or similar. Experience with data science tools and platforms (e.g., Jupyter, Pandas, NumPy, MLFlow). Familiarity with cloud-based AI tools and infrastructure, especially within the AWS ecosystem. Strong understanding of data structures, algorithms, and statistical analysis. Experience working with ETL pipelines and structured 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 ❯
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 ❯
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 ❯
City of London, London, United Kingdom Hybrid / WFH Options
Enertek Group
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 ❯
City of London, Greater London, UK Hybrid / WFH Options
Experis
of experience in software engineering and AI/ML development. Deep proficiency in Python and experience building scalable, distributed systems. Hands-on experience with LLMs, ML lifecycle tools (like MLflow, Weights & Biases), and cloud deployment. A strong grasp of machine learning algorithms, model development, and evaluation techniques. Experience with frameworks like LangChain, LlamaIndex, or Hugging Face is a big plus. More ❯
