• Design and deploy real-time voice and vision AI systems for classroom environments. • Evaluate tradeoffs between fine-tuning, RAG pipelines, prompt-based approaches, and tool-using agents. • Build scalable inference systems with strict latency and cost constraints. • Fine-tune and evaluate LLMs, ASR models, and vision/multimodal architectures. • Design robust evaluation frameworks (offline + online). • Implement safety guardrails and bias mitigation strategies. • Own the full ML lifecycle: data ingestion, training, validation, deployment, monitoring, and iteration. • Optimize infrastructure for GPU utilization, latency, and cost efficiency. • Partner with MLOps to build reliable, versioned AI pipelines. • Work closely with Product and Engineering to translate classroom needs into production-ready AI systems. • Clearly communicate tradeoffs between model complexity, performance, and cost. • Contribute to long-term AI roadmap and architecture decisions.

This role offers a unique opportunity to lead advanced machine learning research while shaping the product and platform roadmap for AI-driven solutions. You will operate at the intersection of research, product, and customer engagement, translating state-of-the-art ML innovations into actionable platform capabilities, reference architectures, and evaluation standards. The position requires a combination of deep technical expertise, strategic vision, and the ability to influence both technical teams and executive stakeholders. You will collaborate cross-functionally to ensure platform offerings meet real-world ML workloads and customer needs while establishing credibility within the broader ML ecosystem. This role also involves external engagement with academic partners and research labs to drive innovation and maintain a cutting-edge perspective. A successful candidate thrives in a fast-paced, highly collaborative environment, with a strong focus on measurable impact and responsible ML practices. Lead forward-looking machine learning research to influence the platform and product roadmap, converting novel insights into actionable requirements and priorities. Translate ML pipeline advancements—including training, inference, fine-tuning, evaluation, and data workflows—into practical reference architectures and benchmarks. Collaborate closely with Product, Engineering, and ML teams to align platform capabilities with emerging workloads and best practices. Establish technical standards for ML-enabled services, ensuring evaluation rigor, reproducibility, reliability, and responsible AI practices. Engage strategic customers and external partners to convert complex ML use cases into clear functional and non-functional requirements. Provide senior technical guidance during architecture reviews, escalations, and design evaluations, ensuring customer realities inform platform decisions. Build strategic collaborations with universities, research labs, and the broader ML ecosystem to accelerate innovation and maintain technical credibility.

The Optum AI Sr Director, Client Scientific Solutions provides leadership for the development and deployment of applied AI/ML research and production initiatives at healthcare clients. This role leads applied research teams in developing and operationalizing advanced AI models, ensuring scalability and reliability. The Sr Director drives innovation, sets technical standards, and fosters collaboration across product and engineering teams to deliver measurable business impact. Applied Research to Production: Lead applied research teams in transitioning AI models from concept to production, ensuring scalability, reliability, and cost-effectiveness. Oversee integration of research outputs into client production environments with solid governance and performance standards. Collaboration: Partner with product and engineering leaders to translate business needs into technical solutions and foster cross-functional alignment. Innovation Leadership: Drive adoption of advanced AI technologies and methodologies at clients to deliver innovative solutions aligned with organizational priorities. Responsible AI: Implement governance and standards for ethical AI practices, ensuring compliance and transparency in model development and deployment. Ecosystem Development: Establish frameworks and best practices for model lifecycle management, monitoring, and continuous improvement. Talent Strategy: Mentor, develop and train senior technical leaders and build a solid pipeline of AI talent through coaching and development initiatives.

• Architect and deliver end‑to‑end AI/ML solutions on Google Cloud using Vertex AI (Workbench, Pipelines, Training, Model Registry, Online/Batch Prediction, Feature Store, Model Monitoring) and Gemini/LLM services—optimized for performance, cost, and maintainability. • Develop production data pipelines with BigQuery, Dataflow, and Dataproc; integrate streaming via Pub/Sub; containerize and orchestrate with Cloud Run and GKE; automate CI/CD with Cloud Build and IaC. • Implement robust MLOps (experiment tracking, evaluation, bias/robustness testing, model versioning, canary/blue‑green rollouts, automated retraining, drift detection, and lineage). • Apply secure‑by‑design patterns—VPC‑SC, private service access, CMEK, fine‑grained IAM, artifact signing, and secrets management—aligned to NIST 800‑53, RMF, and FedRAMP baselines. • Operationalize LLM/GenAI (RAG, tool‑use/agents, safety filters, evaluation harnesses) including retrieval over structured/unstructured data; leverage DoD‑approved AI toolchains where appropriate. • Partner with mission stakeholders to elicit requirements, frame measurable success criteria, and deliver iterative value; provide technical mentorship and lead design/code reviews for engineering teams. • Contribute to program roadmaps (including use cases), documenting architectures, controls, and SOPs for sustained operations.