MLOps Product Visionary

1 week ago

Pune, Maharashtra, India Solytics Partners Full time

Solytics Partners, a pioneer in AI-driven solutions, is seeking an experienced MLOps Product Visionary to spearhead the development and deployment of machine learning models.

Estimated Salary: $140,000 - $180,000 per year

As a key member of our team, you will work closely with cross-functional teams to drive the efficient delivery of ML models in production. Your role will involve identifying market needs, setting the product roadmap, and optimizing the ML lifecycle to ensure high-quality, scalable, and sustainable solutions.

About the Role
  • Develop and communicate a clear product vision and strategy for MLOps, aligning with the overall business goals and technical capabilities.
  • Define and manage the product roadmap, ensuring alignment between development, deployment, and operationalization of machine learning models.
  • Collaborate with data science, engineering, DevOps, and IT teams to design, implement, and optimize MLOps pipelines and workflows that improve model performance, automation, and scalability.

The ideal candidate will have 4-7 years of experience in product management, with a focus on MLOps, AI/ML technologies, or cloud infrastructure. Strong understanding of the ML lifecycle, including model training, deployment, versioning, monitoring, and retraining, is essential. Experience with MLOps tools and platforms, such as Kubeflow, MLflow, TensorFlow Extended, AWS SageMaker, and Azure ML, is highly desirable.

Responsibilities
  • Product Strategy & Vision: Develop and communicate a clear product vision and strategy for MLOps, aligning with the overall business goals and technical capabilities.
  • MLOps Roadmap: Define and manage the product roadmap, ensuring alignment between development, deployment, and operationalization of machine learning models.
  • Cross-Functional Collaboration: Collaborate with data science, engineering, DevOps, and IT teams to design, implement, and optimize MLOps pipelines and workflows that improve model performance, automation, and scalability.
  • Lifecycle Management: Oversee the full ML lifecycle from model development, deployment, monitoring, and continuous improvement.
  • User-Centric Design: Engage with end-users (data scientists, analysts, and operations teams) to gather feedback, identify pain points, and iterate on product features.
  • Metrics & Analytics: Define and track key performance indicators (KPIs) for MLOps, such as model performance, deployment frequency, and uptime.
  • Risk & Compliance: Ensure compliance with relevant industry standards and regulations in the deployment and monitoring of machine learning models.
  • Market Research & Innovation: Stay ahead of industry trends in MLOps, AI/ML technology, and cloud infrastructure to continuously evolve and improve product offerings.
  • Customer & Stakeholder Management: Build strong relationships with customers and internal stakeholders, ensuring alignment between product development and customer needs.

Desired Skills:
• 4 to 7 years of experience in product management, with a focus on MLOps, AI/ML technologies, or cloud infrastructure.
• Strong understanding of the ML lifecycle, including model training, deployment, versioning, monitoring, and retraining.
• Experience with MLOps tools and platforms (e.g., Kubeflow, MLflow, TensorFlow Extended, AWS SageMaker, Azure ML, etc.).
• Familiarity with data pipelines, containerization (Docker, Kubernetes), and cloud platforms (AWS, GCP, Azure).
• Knowledge of CI/CD practices as applied to machine learning models.
• Strong experience in working closely with cross-functional teams (data scientists, software engineers, DevOps, etc.).
• Proven track record of managing complex, high-impact products in a fast-paced, agile environment.
• Excellent communication and leadership skills, with the ability to influence stakeholders at all levels.
• Strong problem-solving ability and analytical thinking.
• Ability to manage multiple priorities and thrive in a dynamic environment.
• Deep understanding of machine learning, AI, and automation processes.
• Familiarity with Agile/Scrum methodologies.

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