Associate Director, AI Engineering

CSL Plasma•Waltham, MA

About The Position

The Associate Director, AI Engineering is the team's senior full-stack engineer and architect, responsible for delivering robust, tested, supportable systems from working prototypes to R&D scientists. This hands-on individual-contributor role owns the engineering standards, development and deployment patterns, and production readiness for every application the team ships. The Associate Director designs, builds, debugs, and reviews production software, uses agentic development tools throughout the day, and makes decisions about how software created by scientists and colleagues becomes durable, validation-ready systems. The role works alongside validated-system procedures and partners with the Associate Director, AI Platform & Enablement, who helps turn proven team patterns into shared platform capabilities. The role sets standards for languages, frameworks, supported versions, architecture, releases, incident response, code review, and production readiness within agreed governance, security, and quality requirements. It productizes prototypes; establishes reusable application-to-platform integrations and development and deployment patterns; and designs and builds full-stack, cloud-native systems. It partners with Quality and computer system validation colleagues on risk-based engineering and validation practices for LLM and agent-based applications, while Quality retains decision authority. It also works directly with R&D scientists, leads productization of flagship applications, coordinates embedded delivery or professional-services engineers, and supports production systems. The Associate Director gains acceptance from platform teams and Cyber for deployment and access patterns, ensures compliance with company quality, security, and IT policies, and brings production-readiness and risk assessments to senior leadership, including recommendations on build versus buy and whether prototypes should be hardened or retired.

Requirements

Bachelor's degree in a physical, life, or quantitative science or in engineering, or equivalent experience.
10+ years building and operating production software, including a substantial period as a hands-on architect or principal-level engineer who thinks in systems.
Experience building or operating software in regulated, validated environments, such as GxP, computer system validation, IQ/OQ/PQ, or requirements traceability.
Demonstrated, sustained daily use of agentic development tools such as Claude Code or Codex to design, build, and ship software, with full ownership of generated code, evidenced by delivered professional or personal software.
Experience owning CI/CD, infrastructure as code, and release engineering with a globally distributed team, including pipelines, environments, deployment and rollback, and observability.
Track record of taking inherited or prototype code to production grade through refactoring, test coverage, security hardening, performance improvements, and operational readiness.
Experience solving production concerns between applications and platforms, such as authentication and SSO, secrets, networking, and access control, and turning solutions into reusable patterns.
Fluency in at least one production language and judgment to direct agents across a polyglot stack; the team's stack is Python, TypeScript, and PostgreSQL.
Deep cloud-native experience, including containers and serverless, workflow orchestration, IAM and least-privilege design, and managed data stores.

Nice To Haves

Advanced degree.
AWS experience is preferred; equivalent depth in Azure or GCP is considered.
Hands-on experience with LLM application patterns such as RAG, vector search, and multi-stage pipelines.
Experience working directly with research scientists.

Responsibilities

Own engineering standards and production readiness for every application the team ships, including selecting and maintaining standard languages, frameworks, and supported versions.
Make architecture, release, and incident-response decisions within agreed governance, security, and quality requirements.
Assess prototypes against engineering standards and refactor, strengthen, and productize them with sound architecture, tests, observability, security, documentation, and operability.
Work with stakeholders to recommend and implement decisions to retain, rebuild, or retire prototypes.
Own application-to-platform integration concerns, including authentication and single sign-on, secrets, networking, deployment, and monitoring; develop reusable patterns and collaborate with platform partners to move proven patterns into the platform.
Establish and maintain development and deployment patterns, including CI/CD pipelines, infrastructure as code, environments, starter templates, monitoring, and operational runbooks.
Own code-review standards and the merge gate for team repositories; review and approve changes from scientists, colleagues, contractors, and embedded delivery engineers.
Run design and architecture reviews before applications enter shared environments and set review standards for AI-generated code.
Design and build full-stack, cloud-native systems, including APIs, orchestration and workflow services, data and vector stores, identity and access, and scientist-facing front ends.
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 supporting evidence case.
Work directly with R&D scientists to define software requirements, review code, and build engineering capability through pairing.
Lead productization of flagship applications, direct embedded delivery or professional-services engineers, and coach colleagues on engineering practice.
Gain platform-team and Cyber acceptance for deployment and access patterns; implement application security practices in line with Cyber standards and ensure compliance with company quality, security, and IT policies.
Own the production-support approach, including triage, escalation, incident follow-up, and engineering fixes, with responsibilities agreed across the team and platform partners.
Present production-readiness and risk assessments to senior leadership, with recommendations on build versus buy and which prototypes to harden or retire.