ML Ops Lead
Job Title: ML Ops Lead
Location : USA Remote
Visa: OPT EAD EAD GC
Mode: C2C
Job Details:
As an ML Ops Lead, you will be responsible for overseeing the end-to-end lifecycle of machine learning models, ensuring their seamless integration from development to deployment in production environments. This role requires a strong technical background in machine learning, deep understanding of deployment practices, and leadership skills to drive efficient ML operations.
Job Responsibilities:
Strategy and Planning:
- Develop and implement ML Ops strategies to streamline model deployment and monitoring processes.
- Collaborate with stakeholders to align ML initiatives with business goals and objectives.
- Define best practices for continuous integration and deployment (CI/CD) pipelines specific to ML models.
Deployment and Automation:
- Lead the deployment of machine learning models into production environments, ensuring scalability, reliability, and efficiency.
- Automate model testing, monitoring, and retraining processes to maintain model performance over time.
- Implement infrastructure as code (IaC) and containerization techniques (e.g., Docker, Kubernetes) for efficient model deployment.
Team Leadership:
- Manage a team of ML engineers and data scientists, providing guidance on model deployment strategies and best practices.
- Foster a culture of collaboration, innovation, and continuous improvement within the ML Ops team.
- Conduct performance evaluations, mentorship, and career development for team members.
Technical Expertise:
- Architect scalable and robust ML infrastructure solutions, including data pipelines, model serving layers, and monitoring dashboards.
- Implement security and compliance standards in ML workflows, adhering to regulatory requirements.
- Stay updated with the latest advancements in ML Ops tools, technologies, and methodologies.
Communication and Collaboration:
- Communicate complex technical concepts to non-technical stakeholders, including executives and business leaders.
- Collaborate with cross-functional teams including data engineering, software engineering, and product management to integrate ML solutions into business applications.
- Lead meetings, workshops, and training sessions to disseminate ML Ops knowledge across the organization.
Skills Required:
- Proven experience (minimum 5 years) in machine learning engineering or ML Ops roles, with a track record of deploying models in production environments.
- Expertise in Python, including libraries such as scikit-learn, numpy, pandas, and a deep learning framework (e.g., Pytorch, TensorFlow).
- Strong understanding of containerization (Docker, Kubernetes), CI/CD pipelines, and cloud platforms (AWS, GCP, Azure) for deploying ML models.
- Experience with version control systems (e.g., Git), DevOps practices, and infrastructure automation tools (e.g., Terraform, Ansible).
- Knowledge of model monitoring and retraining techniques, as well as data governance and security principles in ML workflows.
- Excellent problem-solving skills and ability to troubleshoot complex issues related to model deployment and performance.
- Leadership skills with the ability to mentor and inspire a team, drive initiatives, and communicate effectively across organizational levels.
Other Requirements:
- Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
- Demonstrated ability to manage multiple projects and stakeholders in a dynamic environment.
- Strong analytical and organizational skills with attention to detail.
- Willingness to stay updated with industry trends and continuously learn new technologies and methodologies.
Additional Information:
- This position offers opportunities for career growth, leadership development, and contributions to cutting-edge AI initiatives.
- Flexible work arrangements may be available based on location and company policies.
0 Comments