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Principal Applied Scientist

Microsoft· Applied Sciences
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Hyderabad, Telangana, IndiaFull-timePosted 1w ago

About the role

Microsoft Cloud Operations and Innovation (CO+I) seeks a Principal Applied Scientist to join the Data Center Applied AI team. The role focuses on advancing and integrating multimodal agentic AI systems into core tools and operational workflows for data center operations, bridging state-of-the-art AI research with production-grade engineering to deliver reliable, secure, scalable, observable AI solutions.

Responsibilities

  • Advance Applied AI Research to Improve Quality and Reliability: Apply deep expertise in Generative AI, deep learning, NLP, and multimodal models to translate cutting-edge research into high-impact, production-ready AI solutions. Design and execute experiments that measurably improve agent planning, memory, grounding, reasoning, and long-horizon task completion. Perform lightweight fine-tuning (e.g., LoRA and related techniques) on multimodal and large multimodal language models—to improve entity recognition, reasoning accuracy, and response quality. Implement state‑of‑the‑art approaches using foundation models, advanced prompt engineering, RAG, knowledge graphs, and multi‑agent architectures, complemented by classical ML techniques where appropriate.
  • Build and Ship Scalable Agentic AI Systems: Architect and implement end‑to‑end agent workflows that decompose user intent into executable plans, intelligently select and invoke tools, and recover gracefully from errors and edge cases. Rapidly prototype solutions and partner with engineering teams to drive production deployment, including debugging live systems and building AIOps workflows. Use data and telemetry to identify AI quality gaps, generate insights, and deliver proofs of concept that apply research innovations to real-world operational challenges. Design robust multi-step reasoning and tool-use strategies, including function calling, code execution, APIs, and secure connectors, with strong safety and reliability guardrails. Drive production excellence across latency, reliability, cost efficiency, observability, monitoring, and safe fallback behaviors.
  • Own Evaluation, Metrics, and Iteration Loops: Define task-level evaluation metrics for agentic behavior, including success rate, tool-call accuracy, step efficiency, hallucination rate, safety violations, time-to-completion, and user satisfaction. Build and maintain offline and online evaluation pipelines, including golden datasets, scenario simulators, regression test suites, human-in-the-loop evaluation frameworks, rubric design, A/B experimentation and telemetry-driven iteration loops.
  • Collaborate Across Disciplines and Lead Technical Execution: Partner with business and engineering stakeholders to translate requirements into clear technical specifications, research plans, and delivery milestones. Lead design reviews and influence engineering decisions across agent frameworks, model integration, and system architecture. Mentor and support other applied scientists and engineers, raising the bar for applied AI execution and production quality.

Qualifications

  • PhD in Computer Science, Machine Learning, AI, NLP, or related field, OR 10 years of experience in delivering production grade AI systems.
  • 10+ years of hands-on experience applying NLP, and/or LLMs to real-world production problems.
  • Strong foundation in Large Language Models (LLMs), Generative AI, deep learning, and modern NLP.
  • Proven experience building production-grade LLM systems, including prompt engineering, RAG, and multi-step reasoning pipelines.
  • Solid expertise in chunking strategies and embedding selection, including tradeoffs in retrieval quality, context size, and downstream task performance.
  • Experience designing and operating vector databases, including indexing strategies, similarity metrics, and retrieval optimization.
  • Experience supporting production operations (AIOps/MLOps), including monitoring, logging, quality metrics, and debugging live systems.
  • Proficiency in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow) and model serving/inference.
  • Strong applied experimentation skills: defining metrics, analyzing results, and iterating based on data.
  • Ability to collaborate effectively with engineering, product, and business stakeholders.

Skills

Required:Generative AIAgentic AI systemsRAG and knowledge graphsMLOps/AIOps
Last updated Oct 16, 2026. Interested? Apply on the company site.

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