Associate Director, Scientific Computing and AI Engineer

Key Accountabilities / Core Job Responsibilities

• Scientific Computing & HPC Platform Engineering:
  Lead the architecture, build‑out, and ongoing optimization of on‑premise GPU clusters, hybrid cloud HPC environments, and supporting storage and networking infrastructure.
• Partner with research scientists to profile workloads, size infrastructure, and iteratively improve job performance and researcher self‑service capabilities.
• Design, operate, and support compute environments for computationally intensive workloads such as molecular dynamics (Schrödinger), CryoEM, genomics, structural biology, and AI model training.
• Implement job scheduler configurations (Slurm/LSF), parallel file systems, and interconnect optimization to maximize throughput and utilization for scientific users.
• Architect cloud‑burst strategies for elastic scaling of peak HPC demand and ML training workloads.
• Applied AI Engineering & Generative AI Solutions:
  Design and engineer production AI/ML systems and generative AI solutions spanning cloud infrastructure, data pipelines, vector databases, RAG architectures, and LLM application layers.
• Build and deploy agentic AI workflows that automate or augment scientific processes.
• Develop and maintain AI evaluation frameworks, prompt engineering standards, and model lifecycle management practices (MLOps) that ensure reliable, auditable outputs in a GxP‑adjacent environment.
• Prototype and pilot emerging AI capabilities (AI agents, digital twins, foundation model fine‑tuning) and transition proven solutions to production at scale.
• Collaborate with cross‑functional stakeholders to scope AI use cases, define success criteria, and demonstrate concrete business value through working proof‑of‑concept and production deployments.
• Implement Infrastructure as Code and CI/CD pipelines with integrated security and compliance controls.
• Technical Leadership & Architecture Guidance:
  Serve as the senior technical partner for scientific computing and AI platform decisions; set engineering standards, reference architectures, and technology guardrails in collaboration with Enterprise Architecture.
• Mentor and develop engineers across the IT organization and elevate team‑wide engineering practices.
• Translate complex technical concepts for non‑technical stakeholders including senior leadership and R&D scientists.
• Evaluate vendor and open‑source technologies; lead proof‑of‑concept assessments and build‑vs‑buy recommendations for new platform capabilities.
• Participate in architecture review processes to ensure cross‑functional alignment and long‑term platform coherence.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a closely related field.
• 10–12+ years of progressive experience in platform engineering, scientific computing, or infrastructure engineering, with at least 3 years operating at a senior/principal individual‑contributor level.
• Deep, hands‑on expertise in two or more of the following: HPC cluster administration and optimization (Slurm/LSF, parallel file systems, GPU/CUDA environments); cloud platform engineering at production scale; AI/ML platform engineering including model deployment, MLOps pipelines, and LLM application development.
• Generative AI and agentic system design using modern frameworks (Lang Chain, Lang Graph, Auto Gen) and foundation models.
• Proficiency in Infrastructure as Code and CI/CD tooling.
• Strong Python and scripting skills.
• Experience working in or supporting regulated biotech, pharmaceutical, or life sciences environments with exposure to GxP, 21 CFR Part
  11, or equivalent data integrity frameworks.
• Demonstrated ability to lead technical initiatives end‑to‑end—from architecture through production delivery—in a lean, resource‑constrained organization.
• Hands‑on…