Senior Manager-Machine Learning

The Business & Industry Copilots group is a rapidly growing organization that is responsible for the Microsoft Dynamics 365 suite of products, Power Apps, Power Automate, Dataverse, AI Builder, Microsoft Industry Solution and more. Microsoft is considered one of the leaders in Software as a Service in the world of business applications and this organization is at the heart of how business applications are designed and delivered. 

This is an exciting time to join our group Customer Zero Engineering and work on something highly strategic to Microsoft. The goal of Customer Zero Engineering is to build the next generation of our applications running on Dynamics 365, AI, Copilot, and several other Microsoft cloud services to deliver high value, complete, and Copilot-enabled application scenarios across all devices and form factors. We innovate quickly and collaborate closely with our partners and customers in an agile, high-energy environment. Leveraging the scalability and value from Azure & Power Platform, we ensure our solutions are robust and efficient. If the opportunity to collaborate with a diverse engineering team, on enabling end-to-end business scenarios using cutting-edge technologies and to solve challenging problems for large scale 24x7 business SaaS applications excite you, please come and talk to us!  

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. 

Job Description:

We are seeking a seasoned Machine Learning Scientist / MLOps Lead Engineer to join our team. This role is designed for someone who has deep technical expertise in machine learning and MLOps and can provide leadership and strategic direction for building scalable, secure, and robust machine learning systems. The ideal candidate will lead the design, implementation, and operationalization of large-scale machine learning solutions, while also mentoring team members and collaborating with cross-functional teams to deliver impact.

Responsibilities

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.

Qualifications

• 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.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

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