Lead MLOps Engineer

Responsibilities:

• Pipeline Management: Design, implement, and optimize MLOps/DevOps pipelines to enhance the efficiency of machine learning model deployment.
• Cloud Infrastructure: Manage and oversee cloud infrastructure across AWS, Azure, and Google Cloud, ensuring scalability and security.
• Containerization and Orchestration: Lead containerization efforts and manage Kubernetes orchestration to support seamless application deployment.
• Automation and Security: Drive automation initiatives and implement security best practices across all systems and processes.
• Strategic Architecture: Provide strategic guidance on architectural designs for scalable and efficient MLOps/DevOps solutions.
• Team Leadership: Lead and mentor teams through complex DevOps and MLOps projects, ensuring timely delivery and adherence to best practices.
• Collaboration: Work closely with data scientists, software engineers, and other stakeholders to enhance product delivery and streamline workflows.
• Continuous Improvement: Stay updated on industry trends and emerging technologies to drive continuous improvement within the organization.
• Capacity Planning: Conduct regular capacity assessments to ensure that cloud infrastructure and resources are aligned with current and future workload demands. Analyze usage trends and performance metrics to make informed decisions about scaling resources, optimizing costs, and maintaining high availability for ML models and applications.

Managerial and Leadership Responsibilities:

• Team Leadership: Inspire and guide a diverse team of engineers, fostering a collaborative and high-performance work environment that encourages innovation and accountability.
• Strategic Vision: Develop and implement a strategic vision for MLOps and DevOps initiatives that align with organizational goals, ensuring that projects deliver maximum business value.
• Resource Management: Oversee resource allocation to ensure optimal staffing and project execution, balancing workloads effectively to meet deadlines.
• Mentorship and Development: Actively mentor team members, providing support for their professional growth through coaching, training, and performance feedback.
• Risk Assessment: Identify potential risks and challenges related to MLOps and DevOps projects, developing proactive mitigation strategies to address them.
• Continuous Improvement: Promote a culture of continuous improvement, encouraging the team to adopt best practices and innovative solutions to enhance efficiency and effectiveness.

Requirements:

• Proven experience in MLOps and DevOps, with a strong background in cloud technologies (AWS, Azure, and Google Cloud).
• Proficiency in tools such as Kubeflow, LitFlow, ONNX, and containerization technologies (Docker, Kubernetes).
• Experience with enterprise-level infrastructure, including tools like Kong, JFrog, and on-premises servers.
• Previous experience in fintech or telecommunications (e. g., Paytm, PayU, VOIP infrastructure) is highly desirable.
• Strong leadership and project management skills, with the ability to guide teams through complex projects.
• Excellent communication and collaboration skills, with a focus on cross-functional teamwork.