• Build and operate the LLM Platform: Develop model routing, prompt registry, and orchestration services for multi-model workflows. • Integrate external LLM APIs (OpenAI, Anthropic, Mistral) and internal finetuned models. • Enable fast, safe experimentation: Implement automated evaluation pipelines (offline + online) with golden sets, rubrics, and regression detection. • Support CI/CD for prompt and model changes, with rollback and approval gates. • Collaborate cross-functionally: Partner with product pods to instrument RAG pipelines and prompt versioning. • Work with deep learning and data teams to integrate structured and unstructured retrieval into LLM workflows. • Optimize performance and cost: Profile latency, token usage, and caching strategies. • Build observability and monitoring for LLM calls, embeddings, and agent behaviors. • Ensure reliability and safety: Implement guardrails (toxicity, PII filters, jailbreak detection). Maintain policy enforcement and audit logging for AI usage.

• Design and operate ML infrastructure: Manage data, training, serving, and inference systems for high-throughput model workflows • Build scalable pipelines: Implement reproducible training and evaluation pipelines with versioning, scheduling, and artifact tracking • Optimize compute and cost: Tune GPU and CPU workloads, manage clusters, and drive efficiency via rightsizing, spot scheduling, and caching • Serve models in production: Operate APIs for low-latency inference with autoscaling, blue-green or canary rollouts, and rollback safety • Ensure reliability and observability: Define and own SLOs; instrument pipelines and services to track latency, cost, drift, and data quality • Secure and automate: Manage IAM, secrets, and container security; automate deployment pipelines via CI/CD and infrastructure as code • Collaborate cross-functionally: Partner with research scientists and AI engineers to deliver models from experiment to production with minimal friction • Document and enable: Build templates, runbooks, and internal tooling that make ML workflows repeatable, safe, and fast

• Own multi‑cloud provisioning, upgrading, and reliability of the core platform • Automate everything via infrastructure as code and GitOps • Ship fully automated deployments across regions • Standardize developer experience across areas such as tooling and CI/CD • Optimize our stack’s scalability and maintain cost‑awareness for AI workloads

• Develop and maintain business applications using Power Apps (Canvas and/or Model-Driven Apps) • Design and implement workflows and business process automation using Power Automate • Work with Dataverse: Data modeling and relationships, Business rules and logic, Security roles and access management • Integrate external systems using REST APIs (HTTP / JSON) • Troubleshoot, debug, and optimize existing solutions • Collaborate with stakeholders to gather requirements and translate them into technical solutions • Ensure best practices in performance, scalability, and security