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. 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. Maintain clear documentation of experiments, results, and methodologies. Share findings through internal forums, newsletters, and demos to promote innovation and knowledge sharing. 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. Deep understanding of small and large language models architecture, Deep learning, fine tuning techniques, multi-agent architectures, 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. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. Experience developing and deploying live production systems, as part of a product team. Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND extensive related experience (e.g., statistics, predictive analytics, research) - OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) - OR equivalent experience. Experience creating publications (e.g., patents, libraries, peer-reviewed academic papers). Experience presenting at conferences or other events in the outside research/industry community as an invited speaker. Experience conducting research as part of a research program (in academic or industry settings). Experience developing and deploying live production systems, as part of a product team. Experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping. Experience across the product lifecycle from ideation to shipping. This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. *
