Overview

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.

Responsibilities:
Key Responsibilities:
Model Development & Deployment:
Collaborate with data scientists and engineers to design, build, and deploy machine learning models at scale.
Develop and maintain MLOps/AIOPs pipelines to automate the end-to-end lifecycle of machine learning models (from development to deployment, monitoring, and retraining).
Work on the integration of models into production systems while ensuring scalability, security, and performance.
Model Operationalization:
Implement CI/CD pipelines for ML models, ensuring smooth deployments with mínimal downtime.




Design and deploy robust monitoring and alerting systems for ML models in production to detect issues such as model drift or data skew.
Implement model governance, version control, and logging systems to ensure compliance with internal standards and external regulations.
Optimization & Scalability:
Optimize machine learning models and pipelines for performance and cost efficiency (compute, storage).
Manage infrastructure for ML workloads using cloud-native tools (Azure, Kubernetes, Docker) or other container orchestration platforms.
Collaboration & Communication:
Partner with cross-functional teams, including Data Engineering, Product Management, and other Engineering teams to build cohesive solutions.
Provide technical guidance to junior engineers and drive best practices for MLOps/AIOPS within the team.
Security & Compliance:
Work on securing models, data pipelines, and infrastructure in compliance with Microsoft's security standards.
Ensure that the entire ML lifecycle adhere

📌 Data Scientist Bengaluru
🏢 Microsoft
📍 Bengaluru