Senior Advisor, Agentic AI Solutions Engineer

Key Responsibilities

• Partner with business functions across DDCS to identify, prioritize, and scope high-value opportunities where AI, machine learning, and automation can improve speed, productivity, insight generation, and decision quality.
• Translate stakeholder needs into practical AI tools, technical designs, acceptance criteria, and delivery plans that fit real scientific, engineering, and operational workflows.
• Develop AI-enabled applications, services, and workflows that integrate models, data sources, document collections, and user-facing interfaces for decision support and workflow automation.
• Create reusable scientific agent skills, task harnesses, validators, run ledgers, and reproducibility controls that allow AI agents to execute diverse, long-running tasks reliably.
• Design agentic knowledge extraction and question-answering systems for structured and unstructured technical content, including PDFs, Word documents, handwritten notes, design histories, experimental records, and regulatory-relevant evidence.
• Apply knowledge graphs, data ontologies, and structured knowledge representation where they improve retrieval, traceability, and reuse.
• Contribute to DDCS data and AI strategy by identifying reusable patterns, data needs, platform capabilities, and solution architectures that support digital transformation at scale.
• Turn information from experiments, simulations, development documents, and business processes into faster insights, stronger judgment, and improved ways of working across innovation and commercialization efforts.
• Communicate model predictions, evidence, assumptions, limitations, uncertainty, and recommended actions through clear visualizations, decision-support outputs, and quantitative business cases that influence solution adoption, workflow redesign, platform investments, and portfolio priorities.
• Champion software engineering best practices including version control, automated testing, CI/CD, containers, documentation, reproducibility, observability, and fit-for-purpose MLOps/agent-ops practices.
• Develop validation, monitoring, documentation, and model-risk approaches aligned with intended use, responsible AI principles, GxP awareness, and regulatory expectations where applicable.
• Leverage cloud infrastructure (and HPC/GPU resources where needed) to develop, test, deploy, and scale agentic workflows, document intelligence systems, and analytics applications.
• Partner across the DDCS matrix with drug delivery scientists, device engineers, formulation scientists, data scientists, AI application engineers, quality, clinical, regulatory, and business stakeholders.
• Identify and prioritize high-impact opportunities where AI solutions, scientific ML, agentic workflows, or knowledge extraction can reduce development time, improve productivity, or mitigate technical and business risks.
• Translate complex analytical and AI findings into clear narratives and quantitative business cases that influence solution adoption, workflow redesign, platform investments, and portfolio priorities.

Basic Qualifications

• Earned Master’s degree with a minimum 5 years post-degree experience in Computational/Computer Science, Machine Learning, Artificial Intelligence, Engineering, or a related quantitative field (or equivalent experience).
• 2+ years of applied technical work building AI or machine learning solutions in a programming language such as Python/R, with working knowledge of the ecosystem (NumPy, pandas, PyTorch, scikit-learn, or related).
• 3+ years of expertise in strategic thinking, problem framing, and translating ambiguous business or scientific needs into tractable AI, modeling, or computational workflows.
• Demonstrated ability to frame ambiguous business or scientific needs as tractable AI, modeling, or computational workflows.
• Skill in communicating technical recommendations with clearly stated assumptions, uncertainty, and limitations, to scientific, engineering, and business audiences.

Additional Preferences

• Earned PhD in relevant field with 2+ years relevant experience.
• Experience applying AI/ML to healthcare, pharmaceutical, or life-sciences problems (prior biology or life-sciences background not required).
• Strong SQL and relational data modeling, with comfort turning large, messy, unstructured, or incomplete data into reliable, decision-ready output.
• Hands-on experience with cloud platforms and solid engineering practice: Git, containers, CI/CD, and experiment or run tracking.
• Experience with knowledge graphs, ontologies, or structured knowledge representation for technical content.
• Evidence of contribution to significant work, ideally through publications at relevant ML/AI/NLP venues (NeurIPS, ICML, ICLR, ACL, EMNLP) or comparable open-source or applied contributions.
• Fluency with agent frameworks and orchestration (LangGraph, AutoGen, CrewAI, or equivalent) and the primitives underneath them: planner/executor splits, hand-offs, escalation logic, and state management across multi-step or multi-session workflows.
• End-to-end RAG design over messy technical documents: parsing and layout extraction from PDFs, scans, and tables; chunking strategy; hybrid search; reranking; embedding models; and vector stores (pgvector, Pinecone, Weaviate, or similar).
• LLM engineering judgment: context design, tool/function calling (MCP or comparable standards), structured output design at scale, and knowing when to fine-tune versus retrieve versus prompt.
• Evals engineering: golden datasets and benchmarks, automated regression suites, and failure-mode tracking.
• Evidence of a trustworthy agent to deploy.
• LLMOps in production, treating cost, latency, and reliability as engineering constraints with the monitoring to match.
• Guardrail and safety design for autonomous systems: approval gates, rollback logic, hallucination and drift detection, and model-risk thinking for high-consequence decisions.