Senior Engineer- DS/ML

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.  

We are seeking a seasoned Machine Learning Scientist / MLOps 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

ML & AI Development

• Lead the research, design, and development of advanced machine learning and AI models, ensuring high performance, accuracy, and robustness. 
• Develop novel algorithms and architectures, optimizing for real-world deployment constraints such as latency, efficiency, and scalability. 
• Leverage cutting-edge advancements in deep learning, generative AI, reinforcement learning, and large-scale ML systems to push the boundaries of AI innovation.
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Scalable Model Deployment & Optimization

• Build and deploy ML models at scale, ensuring seamless integration into production systems with minimal latency and maximum efficiency. 
• Optimize models for performance and efficiency using techniques such as quantization, pruning, distillation, and hardware acceleration (e.g., GPUs, TPUs, FPGAs). 
• Drive best practices for A/B testing, model evaluation, and hyperparameter tuning to continuously improve model performance. 

ML Architecture & Automation

• Design and implement scalable ML architectures that support real-time inference, batch processing, and hybrid AI workflows. 
• Develop robust pipelines for data preprocessing, feature engineering, model training, and deployment, ensuring high-quality input data and reproducibility. 
• Ensure efficient retraining and model versioning, enabling rapid experimentation and continuous learning in production environments. 

AI Model Governance, Security & Compliance

• Ensure all ML models adhere to security, privacy, and ethical AI standards, including fairness, explainability, and regulatory compliance. 
• Implement techniques for bias detection, adversarial robustness, and secure AI deployment to mitigate risks in real-world applications. 
• Establish best practices for model monitoring, drift detection, and performance tracking, ensuring AI systems remain reliable and effective. 

Cross-Functional Collaboration & AI Strategy

• Work closely with data science, engineering, and product teams to align AI initiatives with business objectives and technical feasibility. 
• Influence the broader AI roadmap, advocating for new methodologies, frameworks, and tools to enhance the impact of ML models. 
• Communicate complex ML concepts and results to senior leadership, product teams, and stakeholders, ensuring alignment on AI strategies and outcomes. 

Research, Innovation & AI Thought Leadership

• Stay at the forefront of AI and ML research, actively exploring new algorithms, architectures, and applications in deep learning, NLP, CV, and more. 
• Lead proof-of-concept (PoC) projects, testing and validating emerging AI technologies for potential production adoption. 
• Contribute to AI research communities, publishing papers, attending conferences, and engaging in collaborations with academia and industry partners. 

Mentorship & AI Talent Development

• Mentor and guide junior and mid-level ML scientists, fostering a culture of innovation, experimentation, and continuous learning. 
• Lead technical deep dives, AI model reviews, and algorithmic discussions, helping the team stay ahead of industry trends. 
• Identify skill gaps and drive AI education initiatives, ensuring the team is proficient in state-of-the-art ML methodologies. 

Qualifications

Required Qualification:

• B.Sc in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, or a related field. 

• Industry Experience: 7+ years of hands-on experience in designing, developing, and deploying machine learning models at scale in production environments. 

• ML & AI Expertise: Strong theoretical and practical knowledge of supervised and unsupervised learning, deep learning, generative AI, reinforcement learning, probabilistic modeling, and large-scale ML systems. 

• Programming & Development Skills: Proficiency in Python with deep expertise in ML frameworks and libraries such as TensorFlow, PyTorch,, Scikit-Learn, Hugging Face, or similar. 

• Model Deployment Experience: Experience in deploying and optimizing ML models in cloud-based environments (Azure, AWS, GCP). 

• Data Handling & Feature Engineering: Expertise in working with large-scale datasets, time-series data, structured/unstructured data, and applying advanced feature engineering techniques. 

• Mathematical & Statistical Proficiency: Strong foundation in linear algebra, probability, optimization, Bayesian inference, and numerical methods. 

• Cross-Functional Collaboration: Ability to work closely with software engineers, product managers, and business stakeholders to translate business needs into AI solutions. 
• Communication Skills: Ability to clearly articulate complex ML concepts, write technical reports, and present findings to both technical and non-technical audiences

Preferred Qualifications: 

• Ph.D. in ML/AI or Related Field: Strong research background with contributions to top-tier ML/AI conferences (NeurIPS, ICML, CVPR, ACL, etc.). 

• Experience with Large-Scale AI Systems: Experience working with LLMs, foundation models, multimodal learning, transformers, and generative AI for real-world applications. 

• High-Performance ML Optimization: Expertise in model compression, quantization, distillation, low-rank adaptation (LoRA), and hardware acceleration (GPUs, TPUs, FPGAs). 

• Cloud & Distributed Computing: Experience with Kubernetes, Spark, Ray, Dask, or other distributed computing frameworks for scalable AI training and inference. 

• Responsible AI & Compliance: Familiarity with fairness, interpretability, privacy-preserving AI (e.g., differential privacy, federated learning), and AI governance frameworks. 

• End-to-End AI Product Development: Experience integrating ML models into real-time applications, APIs, or enterprise software solutions. 

• Patents & Publications: Demonstrated contributions to AI innovation through patents, research papers, or open-source projects. 

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.

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