Bringing the State of the Art to Products

 - Build collaborative relationships with product and business groups to deliver AI-driven impact

- Research and implement state-of-the-art using foundation models, prompt engineering, RAG, graphs, multi-agent architectures, as well as classical machine learning techniques.

 - Fine-tune foundation models using domain-specific datasets.

 - Evaluate model behavior on relevance, bias, hallucination, and response quality via offline evaluations, shadow experiments, online experiments, and ROI analysis.

- Build rapid AI solution prototypes, contribute to production deployment of these solutions, debug production code, support MLOps/AIOps. Contribute to papers, patents, and conference presentations.

- Translate research into production-ready solutions and measure their impact through A/B testing and telemetry that address customer needs.

- Ability to use data to identify gaps in AI quality, uncover insights and implement PoCs to show proof of concepts.

 Leveraging Research in real-world problems

- Demonstrate deep expertise in AI subfields (e.g., deep learning, Generative AI, NLP, muti-modal models) to translate cutting-edge research into practical, real-world solutions that drive product innovation and business impact.

- Share insights on industry trends and applied technologies with engineering and product teams.

- Formulate strategic plans that integrate state-of-the-art research to meet business goals.

Documentation 

- Maintain clear documentation of experiments, results, and methodologies.

- Share findings through internal forums, newsletters, and demos to promote innovation and knowledge sharing

Ethics, Privacy and Security

Apply a deep understanding of fairness and bias in AI by proactively identifying and mitigating ethical and security risks—including XPIA (Cross-Prompt Injection Attack) unfairness, bias, and privacy concerns—to ensure equitable and responsible outcomes. 

- Ensure responsible AI practices throughout the development lifecycle, from data collection to deployment and monitoring.

 - Contribute to internal ethics and privacy policies and ensure responsible AI practice throughout AI development cycle from data collection to model development, deployment, and monitoring.

Specialty Responsibilities

- Design, develop, and integrate generative AI solutions using foundation models and more.

- Deep understanding of small and large language models architecture, Deep learning, fine tuning techniques, multi-agent architecture, classical ML, and optimization techniques to adapt out-of-the-box solutions to particular business problems

- Prepare and analyze data for machine learning, identifying optimal features and addressing data gaps.

- Develop, train, and evaluate machine learning models and algorithms to solve complex business problems, using modern frameworks and state-of-the-art models, open-source libraries, statistical tools, and rigorous metrics - Address scalability and performance issues using large-scale computing frameworks.

 - Monitor model behavior, guide product monitoring and alerting, and adapt to changes in data streams.

Qualifications

Required Qualifications

- Bachelor’s degree in computer science, Statistics, Electrical/Computer Engineering, Physics, Mathematics or related field AND 10+ years of technical engineering experience and 6+ in AI/ML, predictive analytics, or research (OR) equivalent experience

- 3+ years of experience with generative AI OR LLM/ML algorithms

Other Requirements:

- Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: 

- Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.