Machine Learning Ops Lead

1 month ago

Sleepy Hollow, United States Visionet Systems Inc. Full time

Position Name – MLOps Lead

Location – NY (Sleepy Hollow) and Basking Ridge, NJ. Need to be onsite 3 days a week

In this role, you will lead and drive the implementation and evolution of our MLOps practices. The MLOps Lead will be responsible for bridging the gap between data science and IT operations, ensuring efficient and scalable deployment, monitoring, and maintenance of machine learning models in production. This role requires a strong understanding of machine learning, software engineering principles, and cloud technologies.

Duties and responsibilities:

  • Lead the design, implementation, and optimization of end-to-end MLOps pipelines for machine learning model development, deployment, and monitoring.
  • Collaborate with data scientists, software engineers, and cross-functional teams to integrate machine learning models into production environments and document the processes
  • Define and enforce best practices for version control, testing, continuous integration, and continuous deployment (CI/CD) pipelines specific to machine learning projects.
  • Automate model training, evaluation, and deployment processes to improve efficiency, scalability, and reproducibility.
  • Implement monitoring and alerting mechanisms to ensure the performance, reliability, and security of deployed machine learning models.
  • Manage and optimize cloud infrastructure and resources (e.g., AWS, Azure, GCP) for machine learning workloads.
  • Stay updated on emerging trends, tools, and technologies in MLOps, machine learning, and cloud computing, and evaluate their potential impact on our processes and products.
  • Provide technical leadership, mentorship, and guidance to junior team members on MLOps practices and methodologies.
  • Collaborate with stakeholders to define requirements, prioritize tasks, and deliver solutions that meet business objectives.
  • Ensure compliance with data governance, security, and regulatory requirements related to machine learning model deployment and operations.
  • Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality
  • Data science models testing, validation and tests automation

Qualifications:

  • 7+ years of demonstrable experience in designing and implementing cloud solutions, build MLOps on cloud (AWS, Azure, or GCP)
  • Experienced in building CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Circle CI, Airflow or similar tools in the context of machine learning.
  • Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field. (Master’s degree preferred)
  • Strong proficiency in programming languages such as Python, and familiarity with libraries and frameworks for machine learning (e.g., TensorFlow, PyTorch, scikit-learn). Experience with cloud platforms (e.g., AWS, Azure, GCP) and container orchestration tools (e.g., Docker, Kubernetes).
  • Knowledge of data engineering principles, ETL processes, and data pipeline architectures.
  • Excellent problem-solving skills with the ability to troubleshoot complex issues in production environments.
  • Strong communication skills and the ability to collaborate effectively across teams and stakeholders.
  • Certifications in relevant areas such as AWS Certified Machine Learning Specialty or Google Professional Machine Learning Engineer are a plus.

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