Applied AI Engineer (Python, ML)

- Develop AI applications such as enterprise copilots, search assistants, document intelligence and generation tools, workflow automation agents, predictive models, decision-support tools, and reusable AI components including prompt libraries and solution patterns - Implement Retrieval-Augmented Generation (RAG) pipelines using enterprise data sources like SharePoint, data lakes, document repositories, and research systems - Construct and uphold comprehensive AI/ML pipelines, encompassing data ingestion, feature engineering, model training, evaluation, deployment, and monitoring - Integrate LLMs into business workflows utilizing APIs and platforms such as Azure OpenAI, OpenAI, Anthropic, and AWS Bedrock - Create frameworks for prompt-engineering, grounding, and evaluation to enhance accuracy, reliability, and alignment - Convert business use cases across various domains (e.g., medical affairs, regulatory, commercial, finance) into functional AI prototypes and production-ready applications - Collaborate with Data Scientists to scale models into production systems, and partner with Product Owners and SMEs to refine requirements, acceptance criteria, and success metrics - Deploy and maintain AI solutions on cloud platforms using contemporary APIs and best practices in software engineering - Implement MLOps and LLMOps functions including versioning, monitoring, logging, performance tracking, observability, and cost optimization - Establish guardrails and controls to prevent data leakage, hallucinations, and misuse - Integrate AI solutions with enterprise identity and data-security frameworks, including RBAC, Purview, and associated governance tools - Ensure all AI systems are reliable, scalable, and secure while adhering to data classification regulations, privacy obligations, and AI governance protocols