26 to 50 of 61 Remote MLflow Jobs

cloud architecture patterns. Familiarity with tools and packages including Kubernetes (deployment in production) is expected. A plus would be additional experience with Airflow/Mlflow/Docker/Flink Experience collaborating closely with multi-functional teams, driving forward best practices. Real-time production systems at scale (millions of requests/ ...

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 ...

Streaming) Data products, mesh or federated ownership models applied on a real programme, not just in theory ML and AI platform work: feature stores, MLflow, model serving, and the data foundations behind RAG or agentic use cases Regulated-sector exposure — financial services, healthcare, public sector — and the assurance that comes ...

Excellent understanding of statistics, machine learning methodologies, and model evaluation. Experience delivering AI solutions from proof-of-concept through to production. Exposure to Databricks, MLflow, Spark, or similar cloud-based data platforms. Ability to influence senior stakeholders and lead technical strategy Desirable Experience Experience building multi-agent AI systems ...

deployment Familiarity with MLOps concepts (CI/CD pipelines, model registries, automated testing) — awareness required, not hands‐on ownership Experience with ML lifecycle tooling — MLflow, Azure ML, or equivalent Python proficiency — able to read and review analytical code, contribute where needed Consulting or forward‐deployed engineering background (Palantir, QuantumBlack ...

architecture experience, including LLM hosting, RAG, vector databases, prompt orchestration and guardrails. Knowledge of AI platform tooling such as Azure ML, Amazon SageMaker, MLflow, Kubeflow and Databricks . Ability to evaluate technologies and make evidence-based architectural decisions. Experience designing secure, scalable and cost-optimised AI infrastructure. Strong architecture documentation ...

solvers (OpenFOAM/Simcenter) into ML pipelines for active learning and model refinement. Setup \"physics-aware\" CI/CD and experiment tracking (Kubeflow/MLFlow) that validates physical consistency laws. Skills/Must have Orchestration: Expert-level Kubernetes (AKS/EKS) is essential. ML Frameworks: Strong proficiency in PyTorch ...

Gemini, Claude and OpenAI APIs. Hands‐on experience with AI tools/frameworks (e.g., Braintrust, AutoEval) and/or MLOps tools (e.g., Airflow, MLflow, Vertex AI, or similar). Solid understanding of data engineering practices, including ETL, batch/streaming pipelines, and data quality monitoring. Familiarity with cloud infrastructure ...

Gemini, Claude and OpenAI APIs. Hands-on experience with AI tools/frameworks (e.g., Braintrust, AutoEval) and/or MLOps tools (e.g., Airflow, MLflow, Vertex AI, or similar). Solid understanding of data engineering practices, including ETL, batch/streaming pipelines, and data quality monitoring. Familiarity with cloud infrastructure ...

ambiguity. Desirable MLOps excellence: reproducible pipelines, model versioning, CI/CD, observability, and automated evaluation. Data engineering: proficiency with Databricks, Apache Spark, Delta Lake, MLflow, and SQL; experience integrating datasets and maintaining data quality. Model training and optimisation: experience with pre-training, fine-tuning, distributed training, inference optimisation or adapting … Education: PhD in AI/ML/CS or related field. Beneficial knowledge General tooling and platforms: Databricks, AWS, GCP, GitHub, Docker/Kubernetes, MLflow, Jira. Edge deployments: Nvidia Jetson (e.g. AGX Orin), Raspberry Pi, or other embedded accelerators. Distributed model training & infra: Pytorch DDP, FDSP and TorchTitan, Megatron, Slurm ...

. Familiarity with observability tooling (Prometheus, Grafana, OpenTelemetry) and with exposing it for third‐party components. Exposure to ML orchestration tooling (Flyte, Airflow, MLflow, SkyPilot). Go, and a track record of contributing to open‐source infrastructure projects. Perks & Benefits Indefinite contract. Variable performance bonus. Signing bonus. We offer work ...

with MongoDB or other NoSQL databases Experience with unsupervised methods such as clustering or hidden Markov models Experience with ML lifecycle tooling such as MLflow Experience with anomaly detection A background around boats, cars, bikes or planes We're less interested in ticking off niche experience like maritime or MongoDB ...

binary classification problems Event streaming data engineering tools such as Flink or Spark Experience with MLOps and tools such as Airflow, BentoML and MLFlow Use of Docker CI/CD and SDLC (e.g. JIRA, Git, Jenkins, Octopus) Job type Permanent Skills DataScientist Why Zempler Bank We’re on a journey ...

metrics. Experience creating, managing, and interrogating graph databases (e.g., Neo4j). Experience with maritime, aerial, or other remote-sensing imagery. Exposure to MLOps tools (MLflow, Weights & Biases, Ray etc.) Experience deploying systems with containerisation, both on premises and in the cloud (e.g. using Docker and AWS ECS/ ...

metrics. Experience creating, managing, and interrogating graph databases (e.g., Neo4j). Experience with maritime, aerial, or other remote-sensing imagery. Exposure to MLOps tools (MLflow, Weights & Biases, Ray etc.) Experience deploying systems with containerisation, both on premises and in the cloud (e.g. using Docker and AWS ECS/ ...

metrics. Experience creating, managing, and interrogating graph databases (e.g., Neo4j). Experience with maritime, aerial, or other remote-sensing imagery. Exposure to MLOps tools (MLflow, Weights & Biases, Ray etc.) Experience deploying systems with containerisation, both on premises and in the cloud (e.g. using Docker and AWS ECS/ ...

Managed Services on cloud and on prem AI/ML & MLOps - Open source AI/ML solutions, AIOps automation, model lifecycle management, Kubeflow, MLFlow, KServe, and AI infrastructure on cloud and edge IoT - Ubuntu on embedded devices and/or edge servers, device management, and robotics Cloud - Ubuntu in public ...

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 ...

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 ...

evaluation. Build hands-on proofs of concept and reference implementations that operationalise large-scale LLM and deep learning workloads on Databricks (Mosaic AI, MLflow, Model Serving, Vector Search, Unity Catalog governance), tuned to the fast iteration cycles Emerging DNB customers expect. Lead fine-tuning and model-customisation engagements on open … distributed training, GPU workloads, and the full MLOps lifecycle (tracking, registry, serving, monitoring). Proficiency in Python and the ML ecosystem (PyTorch/Transformers, MLflow, Spark), and comfort building production-quality reference implementations. Experience with the Databricks platform — or the ability to ramp on it quickly — including Mosaic AI, Model ...

have a good knowledge of modern software engineering best practices, microservices, TDD/DDD, common Design Patterns Experience with Databricks, PostgreSQL, Amazon RedShift or MLflow would be great but not essential What's in it for you: As a Backend Software Engineer/Developer (Machine Learning AWS) you will earn ...

Exceptional command of the English language Nice-to-Have We’d also love experience with: Version control for data and artefacts: DeltaLake DVC MLFlow Designing and operating cloud-based systems AWS and/or Databricks Messaging platforms Infrastructure as Code tools such as: Terraform CloudFormation Pulumi Kubernetes and service mesh ...

/IEC 27001, UK GDPR/DPA 2018). Hands-on exposure to one or more platforms: Microsoft Purview, Azure ML, Databricks/MLflow, Azure Fabric, Power BI governance; plus experience with data catalogues (e.g., Collibra, Alation) is a bonus. Exposure to MLOps processes (model registry, approval workflows, monitoring, incidents ...

delivery of ML infrastructure across backend and frontend, while guiding a 5+ engineering team. The role focuses on deploying scalable ML systems, migrating MLflow to AWS SageMaker, and enforcing MLOps best practices, with collaboration across data science, engineering, and operations in a hybrid London or remote Spain setup. #J ...

enterprise data and AI solutions on the Databricks Lakehouse Platform. Engineer production-grade Lakehouse implementations using technologies such as Unity Catalog, Delta Live Tables, MLflow and Lakeflow. Own the hardest technical problems on an engagement - performance tuning, complex migrations, scale and reliability. Set engineering standards for code quality, testing, observability … testing frameworks and deployment automation. Excellent communication and stakeholder management skills. Desirable Databricks certification. Experience delivering AI or Generative AI solutions. Experience with MLflow, Terraform or Databricks Asset Bundles. Experience building reusable frameworks, accelerators or internal platform tooling. Previous technical leadership, mentoring or engineering-standards ownership at senior or principal ...