• Research and develop new neural network architectures (e.g. alternatives or extensions to Transformers, recurrent / hybrid models, long-context systems) • Design and run architecture-level experiments (scaling laws, memory mechanisms, compute trade-offs) • Prototype models end-to-end — from research code to training-ready implementations • Collaborate with inference and systems engineers to ensure architectures are deployable and efficient • Analyze model behavior, failure modes, and inductive biases • Read, reproduce, and extend cutting-edge research papers • Contribute to internal research notes, benchmarks, and open-source efforts (where applicable)

• Own the strategic roadmap for key AI platforms, focusing on zero and low-code agent builders to streamline internal operations. • Manage relationships with AI platform vendors (e.g., Google, OpenAI) to oversee implementation, align roadmaps, roll out new features, and resolve technical bugs. • Provide technical expertise in building AI agents and implementing platforms; mentor and enable the AI Champion advisory group so they can empower their respective departments. • Apply agentic best practices to design and build workflows that automate complex tasks and help run the business more effectively. • Collaborate with other platform owners to test and continually enhance integrations across our tech stack, including Google Workspace, Slack, ServiceNow, and Salesforce. • Gather requirements from "champion" users and partner with the high-code agent team to test and integrate custom-built functionality into vendor platforms. • Help bridge technical gaps between Product Management and the AI Enablement team to ensure platform capabilities meet business needs.

• Production RAG: indexing, retrieval, hybrid search, reranking, query rewriting, grounding, citations • Context Graph: entity resolution + linking + provenance; graph + vector retrieval; supports multi-hop context • LLM orchestration: tool/function calling, structured outputs, routing across model tiers, failure modes • GPU/inference cost optimization: batching, caching/KV reuse, quantization, autoscaling; optimize $/session + latency • Safety + compliance: PII/PHI handling, redaction, audit logs, deterministic replay, hallucination mitigation • LLMOps: eval harness (golden sets, regression, adversarial), monitoring for quality/cost/drift • Design/ship the end-to-end pipeline: retrieve → assemble context → generate → cite → log/monitor • Improve quality and trust via evaluation, feedback loops, and clear evidence-backed outputs • Partner with product, security, and domain teams; write crisp design docs; raise engineering bar • Ship RAG v1 with citations + measurable quality metrics • Deliver Context Graph v1 that improves retrieval on real copilot tasks • Reduce cost/latency with a concrete inference optimization plan shipped to prod.

• Build and deploy enterprise-ready AI solutions using LLMs, other GenAI approaches, and deep learning capabilities. • Scale out AI solutions: optimize performance of solutions, automate deployment and testing • Rapidly prototype and iterate on AI applications using Azure, AWS, and off-the-shelf tools. • Partner with the AI Solutions Architect to ensure scalable, secure, and compliant system design. • Develop APIs and lightweight UIs (e.g., Dash, Flask and others) to deliver AI tools to end users. • Stay current on emerging AI technologies, including vector databases, RAG pipelines, and productivity AI platforms. • Drive delivery of AI components aligned with product roadmaps and business priorities.