12 of 12 Permanent MLflow Jobs in the South East

software development and implementation of AI/ML systems using cutting-edge technologies such as Python, R, Docker, Kubernetes, and tools like Azure Databricks, MLflow, and Google Vertex AI. Highly Valued Requirements Advanced Education: Master's or Ph.D. in big data, artificial intelligence, or related fields. Consulting Experience: Prior work ...

Face Transformers; OpenCV for vision applications. Strong understanding of experimental design, statistical evaluation, benchmarking and model validation. Experiment tracking and reproducibility tools, for example MLflow, Weights & Biases or equivalent. Data wrangling and analysis using Pandas, NumPy, SQL; familiarity with Spark or similar is a plus. Model optimisation and research ...

Databricks. You'll work as a peer of the platform engineering teams. Rigour in running AI in production: experiment tracking and model registry (MLflow), evaluation frameworks and regression testing before release, monitoring and data-drift management, LLM observability and model governance. A track record of owning technical decisions across model ...

Chroma, Weaviate, pgvector, Azure AI Search, or equivalent. Solid understanding of CI/CD pipelines for application code and ML/AI artifacts, including MLflow, DVC, or equivalent. Strong container and orchestration skills with Docker and Kubernetes, including the ability to deploy and troubleshoot AI inference workloads. Public cloud experience ...

including Python, APIs, and distributed systems. Good understanding of MLOps tooling, including CI/CD, monitoring, and pipeline management. Experience with tools such as MLflow, Airflow, Evidently, DVC, Docker, or Kubernetes. Experience working within AWS, GCP, or Azure environments. Previous mentoring or leadership experience with an interest in developing management ...

Data Mesh concepts, data products, or federated data governance models. AI/ML Infrastructure: Exposure to AI/ML data foundations, including feature stores, MLflow, model serving, or retrieval pipelines (RAG). Regulated Sectors: Experience working within regulated environments (Financial Services, Healthcare, Public Sector). How You Work Pragmatic & Objective ...

retraining Experience leading or mentoring engineering squads Hands‐on experience building and training deep learning models in PyTorch Familiarity with MLOps tooling such as MLflow and CI/CD workflows Experience with AWS, Azure, or GCP Experience with sequence models (e.g. transformers, RNNs) or generative models (e.g. diffusion, flow ...

Triton, vLLM, or similar) with real latency and cost constraints(preferred RayServe) MLOps pipeline tooling - training pipelines, model registries, feature stores, and lineage (Kubeflow, MLflow, Feast, Weights & Biases, or equivalents) LLMOps in production - inference serving, prompt/version management, and LLM observability (tracing, evals, drift, guardrails, cost per request) Governing ...

client travel. Preferred Qualifications Experience conducting formal estate assessment, discovery, or technical due diligence work. Experience with MLOps or model lifecycle tooling such as MLflow, model registries, or feature stores. Experience with workflow orchestration tools such as Airflow, dbt, or Databricks Workflows. Experience with data product or data mesh operating ...

Intact Insurance is actively seeking a Machine Learning Engineer to design and operate production-ready ML systems. You will collaborate with Data Scientists and engineers to shape the ML roadmap, build scalable pipelines, and drive ...

RoCEv2, Spectrum-X, and fabric management. Solid expertise with Kubernetes, OpenShift, containerized workloads, GPU orchestration platforms, and AI/ML technologies such as Kubeflow, MLflow, JupyterHub, Run:AI, Rafay, ClearML, and MLOps solutions. Experience with HPC workload scheduling and resource management tools such as Slurm and PBS Pro, as well ...

converting notebooks into modular, tested, production-ready code. Deploy models into batch and real-time environments, managing versioning, promotion, rollback, and scheduled workflows via MLflow and APIs. Implement monitoring and observability, including data and model drift detection, performance alerts, logging, and automated retraining. Collaborate with Data Engineering and Platform teams … Maintain engineering standards, ensuring high-quality testing, documentation, code quality, reproducibility, and operational reliability. Your skills and experience: Experience with ML platforms including Databricks, MLflow, Delta Lake, and cloud environments. Proficient in Python, PySpark, and SQL, following production coding best practices. Understanding of data, distributed ML pipelines, and model deployment ...