Senior Director, Applied AI Solutions

The role combines technical depth, product-minded execution, and people leadership. You will define ARC's applied AI engineering practices — model and prompt lifecycle, evaluation, agent design patterns, solution architecture — and influence the enterprise engineering and architecture standards owned by IT, while remaining close enough to the work to make consequential design decisions and to lead multidisciplinary teams building AI systems for internal CDS and Development users.

Key Responsibilities

• Applied AI engineering roadmap: translate prioritized AI demand into a multi-year applied AI engineering and delivery plan tied to CDS and Development outcomes.
• AI solution design and delivery (0→1): own the design, build, evaluation, and technical validation of AI services, applications, agents, and shared components through first production release.
• Agentic systems and interoperability: guide the responsible use of agentic patterns for bounded, auditable CDS workflows.
• AI engineering excellence: define and uphold ARC's applied AI engineering practices — data contracts, model and prompt lifecycle management, evaluation harnesses, LLMOps/MLOps, automated testing, and reproducible releases.
• Evaluation, observability, and responsible AI: make evaluation a first-class engineering discipline.
• Adoption and value realization: co-own product discovery and experience design with PMx and support adoption alongside CDS workflow owners.
• Team and technical leadership: build, lead, and develop a high-performing team spanning applied AI, machine learning, and software and data engineering.
• Ecosystem stewardship: contribute the AI assessment that informs build, buy, and partner decisions taken jointly with business owners and IT.

Required Qualifications

• Bachelor's degree with 14+ years of relevant experience, Master's degree with 13+ years, or PhD with 12+ years in computer science, engineering, data science, computational science, bioinformatics, or a related quantitative field.
• Meaningful, current hands-on experience developing and maintaining AI systems and applications.
• Substantial experience leading applied AI, ML, or data engineering organizations while remaining technically hands-on.
• Demonstrated delivery of AI products or reusable capabilities from problem framing through validated release, adoption, and value measurement.
• Deep working knowledge of modern AI system design, including foundation models and LLMs, RAG and semantic retrieval, agent and tool orchestration, model selection and routing, structured outputs, evaluation, guardrails, and human-in-the-loop patterns.
• Strong software and platform engineering judgment across APIs, distributed systems, cloud services, data design, security, CI/CD, MLOps/LLMOps, observability, and production reliability.
• Experience implementing AI in environments with rigorous privacy, security, quality, compliance, validation, or regulatory expectations.
• Track record of hiring, developing, and retaining multidisciplinary technical teams and coaching senior engineers and technical leaders.
• Executive-level communication and influence.

Preferred Qualifications

• Experience in pharmaceutical, biotechnology, healthcare, or another regulated scientific domain.
• Experience applying AI to clinical data review, study design or operations, safety, biomarkers, real-world evidence, regulatory evidence, or scientific knowledge workflows.
• Practical experience designing or governing MCP servers and clients, A2A-enabled systems, or comparable open, vendor-neutral interoperability patterns.
• Experience with multimodal biomedical data, ontologies, knowledge graphs, privacy-preserving methods, causal inference, or statistical and ML methods used in clinical research.
• Evidence of external technical leadership through open-source contributions, patents, peer-reviewed work, conference presentations, standards participation, or substantive industry collaborations.