Software Engineer II - AI/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 the 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. 

 Basic Qualifications:  

• Knowledge of machine learning algorithms and concepts (e.g., supervised learning, unsupervised learning, deep learning) as applied to generative AI. 

• 2+ years of professional experience in a technical role developing, training, evaluating and deploying ML solutions at scale for real-world problems 

• 2+ years of experience as a software engineer, developing and shipping software in Python, C#, Java or modern language equivalent. 

• Familiarity with ML frameworks and libraries like TensorFlow, PyTorch, Scikit-learn, Keras, etc. 

• Experience in handling large datasets and working with data processing frameworks (Apache Spark, Hadoop etc.) 

• Hands-on experience with cloud platforms like Azure, AWS or GCP for deploying and scaling machine learning models. 

• Excellent cross-group and interpersonal skills, with the ability to articulate solutions. 

• Bachelors/Master’s degree with relevant course work toward Computer Science, Data Science, Statistics, Machine Learning, Data Mining and equivalent work experience. 

Preferred Qualifications: 

• Experience in designing and implementing MLOps strategies for model deployment, monitoring, and governance. 

• Familiarity in deep learning architectures (Transformers, CNNs, RNNs) and knowledge of Natural Language Processing (NLP) 

• Knowledge of containers (Docker) and orchestration tools like Kubernetes. 

• Strong analytical mind and a confident decision maker 

• Excellent computer science fundamentals in algorithmic design, data structures, and analyzing complexity 

• Ability to balance competing demands and adapt to changing priorities 

• Experience mentoring junior engineers and data scientists, providing technical guidance and code reviews. 

Responsibilities 

• Design, develop, and implement robust and scalable software applications that utilize generative AI techniques. 

• Integrate LLMs and NLP into software solutions for tasks such as content recommendations, summarization, translation, and retrieval. 

• Optimize generative AI models and their performance for specific use cases. 

• Help establish and drive the adoption of good coding standards and patterns.  

• Help identify opportunities to improve and optimize existing systems using generative AI.  

• Stay up-to-date with the latest trends and technologies in generative AI. 

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