Pfizer
Website:
pfizer.com
Company:
https://www.linkedin.com/company/pfizer
Seniority: Mid-Senior level
Industries: Pharmaceutical Manufacturing
Job details:
Join Pfizer Digital’s 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 Pfizer’s 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
- 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 DecisionIQ's product roadmap
- 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
- 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
- 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
- Bachelor's or Master's 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 on Pfizer Careers.
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