ML Architech

Key Responsibilities:

• MLOps Platform Design: Architect end-to-end MLOps solutions to streamline machinelearningworkflows from experimentation to production deployment.
  Framework Expertise: Implement and customize MLOps platforms and frameworks such as Kubeflow, Metaflow, Ray, MLflow, and Azure ML to meet organizational needs.
  Tool Evaluation: Evaluate, benchmark, and select appropriate MLOps tools and frameworks based on project requirements, scalability, and cost-effectiveness.
  Pipeline Automation: Design automated CI/CD pipelines for machine learning models, ensuring smooth transition from development to production.
  Monitoring & Governance: Implement robust monitoring, logging, and governance practices for deployed machine learning models to ensure reliability and compliance.
  Collaboration: Work closely with data scientists, ML engineers, and DevOps teams to define workflows and ensure operational excellence.
  Scalability & Optimization: Ensure scalability and performance of MLOps solutions, optimizing resource utilization on cloud and on-premise environments.
  Cloud Integration: Integrate MLOps workflows with cloud platforms such as AWS, Azure, or Google Cloud Platform.
  Experiment Tracking: Design systems for efficient tracking of model experiments, hyperparameter tuning, and versioning.
  Model Lifecycle Management: Oversee the lifecycle of machine learning models, including training, serving, monitoring, and retraining workflows.
  Knowledge Sharing: Provide technical leadership and guidance to teams on best practices for MLOps implementation and adoption.
  Industry Trends: Stay updated with the latest advancements in MLOps tools, frameworks, and methodologies to ensure cutting-edge solutions.
  Requirements:
  Experience: 8+ years of experience in data science, machine learning, and MLOps, with a proven track record of implementing end-to-end MLOps platforms.
  Framework Expertise: Hands-on experience with MLOps tools and frameworks like Kubeflow, Metaflow, Ray, MLflow, or Azure ML.
  Tool Evaluation: Strong ability to evaluate and compare MLOps tools based on features, scalability, and organizational requirements.
  Programming Skills: Proficiency in Python, Bash scripting, and familiarity with machine learning libraries such as TensorFlow, PyTorch, and Scikit-learn.
  Cloud Platforms: Expertise in cloud services (AWS, Azure, Google Cloud Platform) and their machine learning ecosystems.
  Containerization: Experience with Docker and Kubernetes for containerized deployments.
  Workflow Automation: Knowledge of workflow orchestration tools like Apache Airflow or Prefect.
  Version Control: Familiarity with model versioning and data versioning tools like DVC or Git.
  Data Engineering: Understanding of data pipelines and storage solutions, including SQL, NoSQL, and distributed file systems (e.g., Hadoop, Spark).
  Monitoring Tools: Knowledge of monitoring tools like Prometheus, Grafana, or ELK stack for model and pipeline observability.
  Problem-Solving: Strong analytical skills to identify bottlenecks and optimize workflows.
  Communication Skills: Excellent verbal and written communication skills to collaborate with cross-functional teams and stakeholders.
  Preferred Qualifications:
  Education: Bachelor s or master s degree in computer science, Data Science, or a related field.

Equal Opportunity Employer.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.