Associate Director, AI Engineering

CSL Plasma; King of Prussia (PA); On-site; USD 180,000 - 240,000; Full time

Job summary

CSL Plasma seeks an Associate Director of Software who will be the senior full‑stack engineer and architect for the team. This hands‑on role owns engineering standards, development and deployment patterns, and production readiness for every application the team ships, guiding prototypes toward durable, validation‑ready systems.

You will partner with AI Platform & Enablement to scale patterns into platform capabilities while ensuring security, governance and quality across all production

Qualifications

• Bachelor's (or higher) degree in a physical, life, quantitative science or engineering required.
• Advanced degree preferred for senior leadership and strategy responsibilities.

Responsibilities

• Own engineering standards and production readiness for every application the team ships.
• Refactor prototypes to production grade with sound architecture, tests, observability and security.
• Own application-to-platform integration, including authentication, SSO, secrets, networking, deployment and monitoring.
• Define and maintain CI/CD pipelines, infrastructure as code, environments and runbooks.
• Lead code reviews and architecture reviews to ensure quality and governance.

Skills

Full-stack engineering

Cloud-native architecture

CI/CD

Security standards

LLM/AI integration

Education

Bachelor's degree in physical, life or quantitative science or in engineering

Advanced degree preferred

Tools

AWS

Kubernetes

Terraform

Git

Job description

Deliver robust, tested and supportable systems from the team's working prototypes to R&D scientists. The Associate Director is the team's senior full-stack engineer and architect. The role owns the engineering standards, development and deployment patterns, and production readiness for every application the team ships, and decides how software built with agentic tools by scientists and colleagues becomes durable, validation-ready systems. Its decisions set how quickly prototypes become products, how reliable those products are, and whether they are ready for validation. The role partners with a peer Associate Director, AI Platform & Enablement, who turns proven team patterns into shared platform capabilities over time. This is a hands‑on individual‑contributor role: the Associate Director designs, builds, debugs and reviews production software, working in agentic development tools (e.g., Claude Code, Codex) throughout the day, alongside validated‑system work that follows its own procedures.

Responsibilities

• Own engineering standards and production readiness for every application the team ships. Select and maintain the team's standard languages, frameworks and supported versions based on security, maintainability, enterprise compatibility and what AI-assisted development builds most reliably. Make architecture, release and incident‑response decisions without escalation, within agreed governance, security and quality requirements.
• Take prototypes built by scientists and colleagues with agentic tools to production grade: refactor, strengthen and productize them with sound architecture, tests, observability, security, documentation and operability. Assess prototypes against agreed engineering standards and work with stakeholders to recommend and implement retain, rebuild or retire decisions.
• Own application‑to‑platform integration: the production concerns that recur across applications, such as authentication and single sign‑on, secrets, networking, deployment and monitoring. Solve them for the team, build them once into reusable patterns, and work with the AI Platform & Enablement Associate Director and enterprise platform teams to move proven patterns into the platform.
• Establish and maintain the team's standard development and deployment patterns: CI/CD pipelines, infrastructure as code, environments, starter templates, monitoring and operational runbooks for the team's applications and agents.
• Own code‑review standards and the merge gate for the team's repositories, reviewing and approving changes from scientists, colleagues, contractors and embedded delivery engineers, none of whom report to the role. Run design and architecture reviews before applications enter shared environments, and set the review standard for AI‑generated code.
• Design and build full‑stack, cloud‑native systems: APIs, orchestration and workflow services, data and vector stores, identity and access, and scientist‑facing front ends, favoring managed services and simple, maintainable designs.
• Partner with Quality and computer system validation colleagues to define and implement risk‑based engineering and validation practices for LLM and agent‑based applications, and build the evidence case for them. Practices may include requirements traceability, model and configuration change control, regression evaluations, controlled releases and auditable records. Quality retains decision authority.
• Work directly with R&D scientists and the team's scientists to define software requirements, review their code and build engineering capability through pairing. Lead the productization of the team's flagship applications, directing embedded delivery or professional‑services engineers and coaching colleagues on engineering practice.
• Gain acceptance from platform teams and Cyber for the team's deployment and access patterns. Implement application security practice in line with Cyber standards, and ensure the team's applications and delivery practice comply with the company's quality management system, security guidelines and IT policies.
• Own the production‑support approach for the team's applications, including triage, escalation, incident follow‑up and engineering fixes, with support responsibilities agreed across the team and platform partners.
• Bring production‑readiness and risk assessments to senior leadership, with recommendations on build versus buy and on which prototypes to harden or retire.

Qualifications

• Bachelor's degree in a physical, life or quantitative science or in engineering, or equivalent experience; advanced degree preferred.
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