Principal Applied Scientist

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. Ability to use data to identify gaps in AI quality, uncover insights and implement PoCs to show proof of concepts. 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. 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 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. Monitor model behavior, , guide product monitoring and alerting, and adapt to changes in data streams. Bachelor's degree in Computer Science, Statistics, Electrical/Computer Engineering, Physics, Mathematics or related field AND 8+ years of experience in AI/ML, predictive analytics, or research - OR Master's degree AND 6+ years of experience - OR PhD AND 5+ year of experience 1+ year (s) of experience with generative AI OR LLM/ML algorithms These requirements include but are not limited to the following specialized security screenings: Experience with MLOps Workflows, including CI/CD, monitoring, and retraining pipelines. Familiarity with modern LLMOps frameworks (e.g., LangChain, PromptFlow) 5+ years of experience publishing in peer-reviewed venues or filing patents Experience presenting at conferences or industry events 5+ years of experience conducting research in academic or industry settings 3+ year of experience developing and deploying live production systems 3+ years of experience working with Generative AI models and ML stacks Experience across the product lifecycle from ideation to shipping