Microsoft's mission is to empower every person and every organization on the planet to achieve more by developing world-class, innovative Skilling Products & Experiences that inspire customers, partners, Microsoft Customer and Partner Solutions (MCAPS) sellers, and future generations to achieve more by skilling, upskilling, and reskilling, thereby reaching 100M+ learners.
Requirements
- Bachelor's Degree in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C, Java, JavaScript, or Python
- 3+ years experience in designing, developing, and deploying machine learning models at scale in production environments.
- 3+ years experience within ML & AI knowledge. Specifically theoretical and practical knowledge of supervised and unsupervised learning, deep learning, generative AI, reinforcement learning, probabilistic modeling, and large-scale ML systems.
- 3+ years experience within programming & development: proficiency in Python and command of ML libraries and frameworks such as TensorFlow, PyTorch, Scikit-Learn, Hugging Face Transformers, LangChain, and similar.
- 3+ years experience within model deployment & MLOps: Proven experience deploying ML models in cloud environments (Azure preferred), with familiarity in containerization, continuous integration/continuous development pipelines and model monitoring.
- Experience with LLMs, Retrieval Augmented Generation (RAG) pipelines and agent orchestration frameworks.
- Foundation in linear algebra, probability, and statistical modeling techniques relevant to ML.
Responsibilities
- Lead the research, design, and development of advanced machine learning models and intelligent agents, ensuring performance, reliability, and scalability.
- Develop and fine-tune algorithms across supervised, unsupervised, deep learning, and generative AI domains, with a focus on real-world deployment constraints such as latency and efficiency.
- Apply cutting-edge techniques in LLMs, reinforcement learning, and agent orchestration to enable autonomous, context-aware AI behaviors.
- Build and deploy ML models and agentic systems in cloud environments (preferably Azure), ensuring seamless integration with enterprise platforms and services.
- Optimize models for inference speed and resource efficiency using techniques such as quantization, pruning, distillation, and hardware acceleration (e.g., GPUs, TPUs).
- Implement robust A/B testing, model evaluation, and hyperparameter tuning pipelines to drive continuous performance improvement.
- Design scalable ML and agentic architectures that support real-time inference, batch processing, and hybrid workflows.
Other
- Bachelor's Degree in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics or related technical field
- 4+ years technical engineering experience
- Ability to partner with software engineers, product managers, and business stakeholders to translate business needs into AI-driven solutions.
- Ability to mentor earlier in career and mid-level engineers, fostering a culture of innovation, experimentation, and continuous learning.
- Embody our Culture and Values
