Associate Director, AI Engineering

CSL•Waltham, MA

About The Position

The Associate Director, AI Engineering is the team's senior full-stack engineer and architect, responsible for turning working prototypes into robust, tested, supportable systems for R&D scientists. This hands-on individual-contributor role owns engineering standards, development and deployment patterns, and production readiness for every application the team ships. The role determines how software created with agentic tools becomes durable, validation-ready systems and works alongside validated-system processes that follow their own procedures. The Associate Director designs, builds, debugs, and reviews production software, using agentic development tools such as Claude Code and Codex. The role partners with the peer Associate Director, AI Platform & Enablement, who develops proven team patterns into shared platform capabilities, and works with Quality, computer system validation colleagues, Cyber, enterprise platform teams, R&D scientists, and embedded delivery or professional-services engineers. Quality retains decision authority for validation practices. The role leads productization of the team's flagship applications, directs embedded delivery or professional-services engineers, and coaches colleagues on engineering practice. It owns production support, engineering review standards, and recommendations to senior leadership on production readiness, risk, build versus buy, and whether prototypes should be hardened or retired.

Requirements

Bachelor's degree in a physical, life, or quantitative science or engineering, or equivalent experience.
At least 10 years building and operating production software, with a substantial share 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; evidence may be 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, and operability.
Experience solving production concerns between applications and platforms, such as authentication and SSO, secrets, networking, and access control, and converting 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; 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 all applications shipped by the team; select and maintain standard languages, frameworks, and supported versions based on security, maintainability, enterprise compatibility, and reliability with AI-assisted development.
Make architecture, release, and incident-response decisions within agreed governance, security, and quality requirements.
Refactor, strengthen, and productize scientist- and colleague-built prototypes with sound architecture, tests, observability, security, documentation, and operability; assess prototypes and recommend whether to retain, rebuild, or retire them.
Own application-to-platform integration concerns, including authentication and single sign-on, secrets, networking, deployment, and monitoring; establish reusable patterns and work with platform partners to move proven patterns into shared platforms.
Establish and maintain development and deployment patterns, including CI/CD pipelines, infrastructure as code, environments, starter templates, monitoring, and operational runbooks for applications and agents.
Own code-review standards and repository merge gates; review and approve contributions from scientists, colleagues, contractors, and embedded delivery engineers who do not report to the role.
Conduct 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, favoring managed services and 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 develop supporting evidence; practices may include requirements traceability, model and configuration change control, regression evaluations, controlled releases, and auditable records.
Work directly with R&D scientists and the team's 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 acceptance from platform teams and Cyber for deployment and access patterns; implement application security in line with Cyber standards and ensure applications and delivery practices comply with the company's quality management system, security guidelines, and IT policies.
Own production support for the team's applications, 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.