Senior Director, Engineering

Jobtailor

New York (NY)

On-site

USD 210,000 - 320,000

Full time

2 days ago

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Job summary

Jobtailor is seeking a senior engineering leader to own end-to-end delivery for the Medical AI Acceleration portfolio, translating architecture and product requirements into production-grade software and setting engineering standards.

You will lead CI/CD practices, release management, and LLMOps, supervise three Build Engineers and one Data Integration Engineer, and collaborate with Security, Solution Architecture, Product Management, and platform teams within a matrixed delivery model.

Qualifications

12+ years of progressive software/data engineering experience.
4-5+ years in an engineering leadership or management role.
Experience building and shipping production AI/ML or LLM-based systems at enterprise scale.
Hands-on experience with modern cloud architecture (AWS or Azure), API-first design, and CI/CD/DevOps practice.
Hands-on experience with LLMOps/MLOps tooling, vector databases, RAG pipeline engineering, and agent orchestration frameworks.
Experience with data integration engineering and pipeline development across enterprise systems.
Proven people-management experience, including hiring, coaching, and performance management of engineering talent.
Experience partnering with Solution Architects and Product Managers/Owners in a matrixed delivery model.
Experience with Agile/Scrum delivery frameworks.
Must be able to travel to Pfizer offices, vendor offices, and other team meeting locations when required.
Must be authorized to be employed in the U.S. by any employer.
Requires permanent work authorization in the United States.
U.S. work visa sponsorship is not available now or in the future

Responsibilities

Own end-to-end engineering delivery for the Medical AI Acceleration portfolio.
Translate solution architecture and product requirements into production-grade software.
Set engineering standards, coding practices, and technical quality bars.
Establish and improve CI/CD pipelines, environment strategy, and release management.
Own sprint and iteration execution with the Scrum Master.
Direct the build of Agentic AI systems, LLM integrations, and RAG pipelines.
Oversee data integration engineering across enterprise systems.
Implement and maintain LLMOps/MLOps practices, including deployment pipelines, monitoring, retraining triggers, and performance measurement.
Enforce responsible AI safeguards, including data access controls, prompt injection defenses, and model output monitoring.
Own production reliability, uptime, and incident response.
Partner with Security, Compliance, Solution Architecture, Product Management, and platform teams.
Review architecture blueprints, define and review RFIs/RFPs, and participate in vendor selection.
Represent engineering capacity, velocity, and risk in portfolio governance forums.
Manage, coach, and develop three Build Engineers and one Data Integration Engineer
Build an AI-native engineering culture through hiring, onboarding, and upskilling
Establish engineering community-of-practice norms such as code review, pairing, and documentation

Skills

AI/ML System Development

LLMOps Practices

Data Integration

Agile Delivery

Cloud Architecture

Education

Bachelor’s Degree in Computer Science

Engineering Degree

Tools

Veeva CRM

PromoMats

Salesforce Life Sciences

Vector Databases

Scrum Tools

Monitoring Tools

Job description

Own end-to-end engineering delivery for the Medical AI Acceleration portfolio
Translate solution architecture and product requirements into production-grade software
Set engineering standards, coding practices, and technical quality bars
Establish and improve CI/CD pipelines, environment strategy, and release management
Own sprint and iteration execution with the Scrum Master
Direct the build of Agentic AI systems, LLM integrations, and RAG pipelines
Oversee data integration engineering across Veeva CRM, PromoMats, Salesforce Life Sciences/Marketing Cloud, and related enterprise systems
Implement and maintain LLMOps/MLOps practices, including deployment pipelines, monitoring, retraining triggers, and performance measurement
Enforce responsible AI safeguards, including data access controls, prompt injection defenses, and model output monitoring
Own production reliability, uptime, and incident response
Partner with Security, Compliance, Solution Architecture, Product Management, and platform teams
Review architecture blueprints, define and review RFIs/RFPs, and participate in vendor selection
Represent engineering capacity, velocity, and risk in portfolio governance forums
Manage, coach, and develop three Build Engineers and one Data Integration Engineer
Build an AI-native engineering culture through hiring, onboarding, and upskilling
Establish engineering community-of-practice norms such as code review, pairing, and documentation

Requirements

Bachelor’s degree in Computer Science, Engineering, or related field
12+ years of progressive software/data engineering experience
4-5+ years in an engineering leadership or management role
Experience building and shipping production AI/ML or LLM-based systems at enterprise scale
Hands‑on experience with modern cloud architecture (AWS or Azure), API-first design, and CI/CD/DevOps practice
Hands‑on experience with LLMOps/MLOps tooling, vector databases, RAG pipeline engineering, and agent orchestration frameworks
Experience with data integration engineering and pipeline development across enterprise systems
Proven people‑management experience, including hiring, coaching, and performance management of engineering talent
Experience partnering with Solution Architects and Product Managers/Owners in a matrixed delivery model
Experience with Agile/Scrum delivery frameworks
Must be able to travel to Pfizer offices, vendor offices, and other team meeting locations when required
Must be authorized to be employed in the U.S. by any employer
Requires permanent work authorization in the United States
U.S. work visa sponsorship is not available now or in the future

Core Competencies

Demonstrates expertise in end-to-end engineering delivery for AI systems, with a strong focus on LLMOps/MLOps practices, data integration engineering, and production-grade software development. Proven leadership in managing engineering teams, establishing coding standards, and ensuring production reliability.

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