Director, AI Engineering Tech Lead

Join Pfizer Digitals Commercial Creation Center & CDI organization (C4) to leverage cutting-edge technology for critical business decisions and enhance customer experiences for colleagues patients and physicians. Our team of engineering data science and AI professionals is at the forefront of Pfizers transformation into a digitally driven organization using data science and AI to change patients lives leading process and engineering innovations to advance AI and data science applications from prototypes and MVPs to full production.

As the AI Engineering and Tech Lead you will design guide and lead the delivery team to provide reliable and stable AI solutions. Your team will architect and develop shared artifacts tooling and deployment standards that accelerate both application and AI/ML models for Pfizer Commercial.

This role is the technical anchor for DecisionIQ owning end-to-end product engineering from user requirements through production deployment.

This is a product-focused technical leadership role. You will ensure that every feature shipped meets user expectations is production-grade and is built on solid engineering practices. You also build and mentor a high-performing engineering team in India (Mumbai/Chennai) that delivers with speed quality and autonomy.

ROLE RESPONSIBILITIES

1) AI Engineering Product Delivery & Product Vision

Own the end-to-end engineering delivery of DecisionIQ: from user story refinement and technical design through development testing and production release

Drive product vision alignment by partnering with Product Owners and commercial stakeholders to ensure every feature delivers measurable user value

Lead development of AI/ML-powered features model integration inference pipelines data transformations with a relentless focus on user experience and adoption

Ensure production-ready models across the lifecycle including training monitoring retraining and rollback

Enable parametrized automated and reusable data/model pipelines that accelerate feature delivery and ensure interoperability across the analytics ecosystem

Synthesize data and model outputs into user-friendly visualizations and interfaces that non-technical users can act on confidently

Stay current with emerging AI/ML technologies and evaluate their applicability to DecisionIQs product roadmap

2) Application Development & Stakeholder Collaboration

Partner with global commercial teams brand leads and regional stakeholders to deeply understand user workflows pain points and unmet needs

Translate user requirements into technical specifications ensuring alignment between business intent and engineering execution

Collaborate with cross-functional data scientists to integrate analytical models into production application features

Drive iterative development cycles with rapid prototyping user feedback loops and continuous improvement of DecisionIQ capabilities

Coordinate with enterprise Data and AI Platform teams to leverage shared infrastructure while maintaining product delivery velocity

3) Engineering Excellence & Quality

Establish and maintain CI/CD pipelines automated testing and deployment standards that ensure reliable frequent releases

Define quality standards including test coverage targets release readiness criteria and production monitoring

Leverage observability tools to gain insights into system behavior and proactively address issues

Champion DevSecOps practices: embed security controls and compliance checks into development workflows

4) People Leadership & Team Development

Build and mentor a high-performing team of AI engineers full-stack developers and QA engineers in India

Set technical direction career paths and coaching routines; foster a culture of ownership learning and engineering excellence

Coach direct reports to adopt best practices improve technical skills and achieve professional growth

Lead contractor and vendor support to extend capabilities and maximize delivery efficiency

Drive engineering maturity through design docs architecture decision records (ADRs) code reviews and continuous learning (labs guilds demos)

This role covers a broad spectrum of skills and we encourage you to apply even if you meet partially.

BASIC QUALIFICATIONS

Bachelors or Masters degree in Computer Science Engineering Data Science or related field

12 years in software engineering data science or related technical fields

5 years leading technical teams with people management responsibilities

Strong hands-on experience building and shipping AI/ML-powered products end-to-end

Proficiency in Python with experience in ML frameworks (scikit-learn TensorFlow PyTorch)

Experience with cloud platforms (AWS or Azure) containerization (Docker/Kubernetes) and CI/CD (GitHub Actions)

Hands-on experience with LLMOps frameworks (LangChain MLflow Langfuse) and cloud AI services (SageMaker Bedrock Azure AI Foundry)

Proven experience delivering user-facing data science / AI applications for both technical and non-technical audiences

Experience with complex data sources including anonymized patient data (EMR/Claims)

Strong English communication skills (written and verbal); ability to work across global time zones

PREFERRED QUALIFICATIONS

Advanced degree (MS/PhD) in Computer Science AI/ML Data Science or Engineering

Experience with full-stack web development (React Vue; HTML Tailwind CSS Bootstrap)

Experience with data science platforms (Dataiku DSS SageMaker) and BI/visualization tools (Tableau Power BI Dash Streamlit)

Experience in regulated/compliance-aware environments (GxP HIPAA SOC2)

Background in product management or product-led engineering teams

Certifications: AWS/Azure Professional CKA/CKAD ISTQB HashiCorp Terraform

Work Location Assignment:Hybrid

Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.

To learn more about acceptable and prohibited uses of AI during the recruitment process please review our candidate AI-use guidelines available onPfizer Careers.