1 to 25 of 43 Remote MLflow Jobs

inference and training across GPUs and cloud-based architectures. Ensure security and compliance for ML platforms handling sensitive data. Evaluate and integrate MLOps tools (MLflow, Kubeflow, etc.) to enhance efficiency. Implement monitoring and alerting systems to detect anomalies and maintain model reliability. What We’re Looking For 3+ years of More ❯

inference and training across GPUs and cloud-based architectures. Ensure security and compliance for ML platforms handling sensitive data. Evaluate and integrate MLOps tools (MLflow, Kubeflow, etc.) to enhance efficiency. Implement monitoring and alerting systems to detect anomalies and maintain model reliability. What We’re Looking For 3+ years of More ❯

security best practices . Prior experience with cloud-based lakehouse implementation . Experience with machine learning (ML) and AI frameworks, particularly within Databricks (e.g., MLflow, AutoML, or PyTorch/TensorFlow). Understanding of AI-driven analytics and predictive modeling to enhance BI solutions. Think you can make a difference? WE More ❯

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, PowerShell More ❯

Desirable Experience of Microsoft Fabric Experience of Microsoft Dynamics Experience with data governance and compliance frameworks Proficiency in AI/ML frameworks (TensorFlow, PyTorch, MLflow) Knowledge of Agile and DevOps methodologies Other Stuff Benefits include 25 Days Annual Leave, Medical Insurance, Flexible Hybrid/Home Working, 5% Employer Contribution Pension More ❯

Desirable Experience of Microsoft Fabric Experience of Microsoft Dynamics Experience with data governance and compliance frameworks Proficiency in AI/ML frameworks (TensorFlow, PyTorch, MLflow) Knowledge of Agile and DevOps methodologies Other Stuff Benefits include 25 Days Annual Leave, Medical Insurance, Flexible Hybrid/Home Working, 5% Employer Contribution Pension More ❯

Desirable Experience of Microsoft Fabric Experience of Microsoft Dynamics Experience with data governance and compliance frameworks Proficiency in AI/ML frameworks (TensorFlow, PyTorch, MLflow) Knowledge of Agile and DevOps methodologies Other Stuff Benefits include 25 Days Annual Leave, Medical Insurance, Flexible Hybrid/Home Working, 5% Employer Contribution Pension More ❯

s degree or higher in Computer Science, Engineering, Mathematics, Statistics, or a related field. Management and Technical Expertise: Extensive hands-on experience with platform MLflow and MLOps databricks features. Popular Gen AI LLM models (e.g., GPT-4o, LLaMa, Mixtral), SLMs with Phi/Gamma/Triplex, and frameworks (e.g., LangChain More ❯

PostgreSQL Databricks Containerisation: Docker, Kubernetes CI/CD: Azure DevOps, GitHub Actions Relational databases and data lake architecture Model and data pipeline integration (e.g. MLflow) Microsoft Azure (Functions, Storage, Compute) Monitoring tools (Grafana, Prometheus, etc.) Mentoring and knowledge sharing within the team Senior Engineer - Desirable Skills: Experience in energy supply More ❯

lifecycle and agile methodologies. Proven experience designing, developing, and deploying machine learning models. Experience with debugging ML models. Experience with orchestration frameworks (e.g. Airflow, MLFlow, etc). Experience deploying machine learning models to production environments. Knowledge of MLOps practices and tools for model monitoring and maintenance. Familiarity with containerization and More ❯

environments (AWS & GCP) Strong understanding of MLOps (model monitoring, CI/CD for ML, versioning and deployment), with experience in MLOps frameworks such as MLFlow, ZenML, Kubeflow, Vertex AI and Sagemaker Experience mentoring and coaching junior engineers to enhance team capability Experience with data visualisation and analytics tooling (e.g., Thoughtspot More ❯

optimization. Understanding of vector databases (e.g., Qdrant, Pinecone) and semantic search techniques. Use of MLOps tools for CI/CD pipelines in AI (e.g., MLflow, Kubeflow, SageMaker). AI for Systems Engineering Experience working with SysML, MBSE tools, or digital engineering pipelines. Understanding of how to map or extract system More ❯

do a programming challenge during the interview process. Experience with Jupyter Notebooks, Python Data Science Libraries. Experience with ML-OPS and related tools (e.g., MLFLOW, Sagemaker, Bedrock) and ability to build interactive, insightful dashboards for monitoring ML models. Place of Performance: Hybrid work in San Antonio, TX. Desired Skills (Optional More ❯

applying probabilistic models in real-world applications (e.g., recommendation systems, anomaly detection, personalized healthcare, etc.). Understanding of modern ML pipelines and MLOps (e.g., MLFlow, Weights & Biases). Experience with recent trends such as foundation models , causal inference , or RL with uncertainty . Track record of publishing or presenting work More ❯

deployment. Proficiency with relevant ML libraries and frameworks such as PyTorch, TensorFlow, scikit-learn, HuggingFace or similar. Experience with modern ML tooling, such as MLflow, Jupyter, feature stores, and vector databases. Understanding of software engineering best practices including version control, testing, CI/CD, containerisation, and observability. Familiarity with MLOps More ❯

with a bias for action. What Technologies We Use: Python, dbtLabs, SQL, GitHub Actions (CI/CD) Terraform, Docker, MLOps Platform (VertexAI, Sagemaker and MLFlow) With ecobee, you'll have the opportunity to: Be part of something big: Get to work in a fresh, dynamic, and ever-growing industry. Make More ❯

Evidently, etc.) Experience in building parallelised or distributed model inference pipelines Nice-to-Have Skills Familiarity with feature stores and model registries (e.g. Feast, MLflow, SageMaker Model Registry) Knowledge of model versioning , A/B testing , and shadow deployments Experience implementing or contributing to MLOps frameworks and scalable deployment patterns More ❯

analysis. Expertise in Python and the tools and libraries that make ML magic 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 More ❯

or Ph.D. degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related fields. Proven experience in Databricks and its ecosystem (Spark, Delta Lake, MLflow, etc.). Strong proficiency in Python and R for data analysis, machine learning, and data visualization. In-depth knowledge of cloud platforms (AWS, Azure, Google … skills, with the ability to explain complex technical concepts to non-technical stakeholders. Preferred Skills: Experience with Databricks machine learning workflows and automation (e.g., MLflow). Familiarity with containerization technologies such as Docker and Kubernetes. Experience with version control tools (e.g., Git). Knowledge of data visualization tools like Tableau More ❯

or Ph.D. degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related fields. Proven experience in Databricks and its ecosystem (Spark, Delta Lake, MLflow, etc.). Strong proficiency in Python and R for data analysis, machine learning, and data visualization. In-depth knowledge of cloud platforms (AWS, Azure, Google … skills, with the ability to explain complex technical concepts to non-technical stakeholders. Preferred Skills: Experience with Databricks machine learning workflows and automation (e.g., MLflow). Familiarity with containerization technologies such as Docker and Kubernetes. Experience with version control tools (e.g., Git). Knowledge of data visualization tools like Tableau More ❯

Terraform Monitoring and performance tuning of cloud-based applications and services Nice to haves: (MLOps): Model Deployment & Serving - Deploy and manage ML models using MLflow, Azure ML, SageMaker, or similar, ensuring scalability and performance. Monitoring & Retraining - Set up model drift detection, performance monitoring, and automated retraining. ML Pipelines & CI/ More ❯

PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering More ❯

PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering More ❯

PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering More ❯

PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering More ❯