Microsoft Senior Manager-Machine Learning 

India, Karnataka, Bengaluru 

168057665

• 8+ years of experience in machine learning, MLOps, or software engineering roles, with at least 3 years in a technical leadership or lead engineer role.
• Proven experience designing, building, and operationalizing large-scale machine learning systems in a production environment.
• Expert knowledge of cloud platforms (preferably Azure) for ML workloads, including infrastructure as code, resource provisioning, and cost management.
• Deep expertise in ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and MLOps tools (e.g., Kubeflow, MLflow, Azure ML).
• Strong experience with containerization (Docker, Kubernetes) and microservices architecture.
• Solid understanding of security, privacy, and compliance requirements in machine learning systems (e.g., GDPR, CCPA, Responsible AI).
• Experience leading cross-functional teams and projects, influencing stakeholders, and driving decision-making processes.

Preferred Qualifications:

• Advanced degree in Computer Science, Data Science, or a related field.
• Experience with distributed computing frameworks (e.g., Apache Spark) for large-scale data processing and model training.
• Hands-on experience with advanced machine learning techniques (e.g., reinforcement learning, generative models, transfer learning).
• Strong knowledge of automation and orchestration tools for model monitoring and retraining in production.
• Excellent communication and presentation skills, with the ability to influence technical and business stakeholders.

Key Responsibilities:

1. Technical Leadership:
• Lead and mentor a team of machine learning and MLOps engineers in developing and deploying machine learning models and systems at scale.
• Provide technical leadership across the entire ML lifecycle, from data ingestion to model development, deployment, monitoring, and governance.
• Drive best practices for MLOps, ensuring the team is following modern, scalable, and secure practices for ML model deployment and operations.
2. ML Architecture & Pipeline Design:
• Architect end-to-end machine learning pipelines, ensuring scalability, robustness, and maintainability in production environments.
• Design and implement CI/CD pipelines for ML model training, testing, deployment, and monitoring, with a focus on automation and reducing time-to-market.
• Optimize model performance and cost efficiency through advanced techniques like distributed training, model pruning, and hardware acceleration (e.g., GPUs, TPUs).
3. Model Governance & Compliance:
• Define and enforce model governance policies including versioning, reproducibility, monitoring, and auditing to ensure compliance with regulatory and ethical standards.
• Lead efforts to ensure ML models adhere to Microsoft’s security and compliance guidelines, including privacy, fairness, and responsible AI practices.
• Design frameworks for model validation and drift detection, ensuring the continuous performance of models in production.
4. Cross-Functional Collaboration:
• Collaborate with data science, software engineering, and product teams to integrate ML models into scalable, production-ready systems.
• Influence the broader organization’s ML strategy by advocating for new technologies, tools, and approaches to improve the performance, scalability, and security of ML models.
• Serve as a liaison between technical teams and senior leadership, translating business needs into technical solutions.
5. Innovation & Continuous Improvement:
• Stay current with the latest trends and advancements in machine learning and MLOps to ensure that the team is adopting the best tools and practices.
• Identify bottlenecks and pain points in the current ML workflows, and spearhead initiatives to improve efficiency and effectiveness.
• Lead proof-of-concept (PoC) efforts for new tools, frameworks, or methods to keep the platform cutting-edge.
6. Mentorship & Talent Development:
• Provide mentorship to engineers across the team, fostering a culture of growth and continuous learning.
• Take an active role in talent development, conducting code reviews, guiding architecture decisions, and providing technical feedback.
• Identify skill gaps within the team and create development plans to elevate the team’s technical competencies.