Role Summary

Do you want to make a global impact on patient health? Pfizer's International Commercial division is transforming how we bring our medicines to patients across international markets. Within Business Transformation & Technology, our Data Engineering and AI organization builds and scales AI-powered products for internal commercial clients — solutions that sharpen decision-making, accelerate execution, and free our teams to focus on what matters most: ensuring patients receive the medicines they need.

As a Senior Manager, AI Solutions Tech Lead, you will be the hands-on technical leader for AI products across our International portfolio — orchestrating the build across blended teams of internal engineers and external contractors while staying close to the code. You will bring deep expertise in at least one core discipline — data engineering, data science and ML engineering, AI engineering, or backend/full-stack development — combined with working knowledge across the others, and you will navigate the full digital solution stack with confidence: data products, AI data products, generative and agentic AI solutions, and full-stack applications, understanding what it takes to advance each from POC to MVP to an enterprise-grade system. You will set and enforce engineering standards, drive technical decision-making through structured trade-off analysis and well-reasoned proposals, and safeguard the quality of everything the team ships — reviewing pull requests and contributing features alongside your leadership responsibilities.

Role Responsibilities

• Act as the hands-on tech lead for AI product delivery: orchestrate the build across blended teams of 10+ internal engineers and external contractors, while remaining hands-on through pull request reviews and targeted feature development.

• Set, document, and enforce engineering standards, best practices, and development hygiene across the team; influence adherence across contributors beyond direct reporting lines, including contractor and vendor teams.

• Drive technical decision-making: analyze trade-offs, formulate proposals with clear rationale, and present recommendations to technical teams, product owners, and leadership stakeholders.

• Safeguard solution quality end to end: enforce thorough testing (unit, integration, etc.), code review discipline, and release readiness, applying the engineering rigor appropriate to each maturity stage — from POC to MVP to enterprise-grade production.

• Analyze integrations, complex system dependencies, and cross-solution designs; identify technical debt and improvement opportunities and drive their prioritization and resolution.

• Operate fluently across the digital solution stack — data products, AI data products, generative/agentic AI solutions, and full-stack applications — and guide the team on sound implementation choices for each.

• Coordinate and manage delivery through external vendor and contractor teams alongside internal engineering resources, with emphasis on the quality of deliverables and adherence to team standards.

• Collaborate extensively with product owners, core engineering and data science teams, leadership stakeholders, and contractors; communicate technical concepts clearly to technical and non-technical audiences.

• Stay current with emerging technologies and approaches (including agentic AI); evaluate their applicability and foster a collaborative learning environment within the team.

Qualifications

Basic Qualifications

• Bachelor's degree in a relevant discipline (Computer Science, Data Science, Computer Engineering, Information Systems, or a related field) and 7+ years of hands-on experience in AI, data, or software solution engineering, with a track record of building, deploying, and testing complex software systems.

• Demonstrated technical leadership experience as a tech lead or equivalent, orchestrating teams of 10+ members, including blended internal and contractor teams.

• Experience coordinating with and managing external vendor or contractor teams, with emphasis on delivery quality.

• Deep expertise in at least one relevant discipline (e.g., data engineering, data science and ML engineering, AI engineering, backend/full-stack development), with working exposure across the others.

• Solid understanding of the digital product development lifecycle, including the distinction between POC, MVP, and enterprise-grade systems and the engineering practices each stage requires.

• Strong critical thinking, technical judgment, and trade-off analysis; ability to formulate proposals, analyses, and decision rationale.

• Fluency in Python and proficiency in SQL; ability to write clean, efficient, and maintainable code.

• Experience working in a cloud-based analytics ecosystem (e.g., AWS, Snowflake) and familiarity with modern data tooling (e.g., Airflow, dbt) and the standard AI engineering stack.

• Solid grounding in cloud infrastructure and backend engineering, CI/CD, and testing practices; awareness of MLOps/LLMOps.

• Hands-on experience working in agile teams, processes, and practices, and proficiency with agile delivery tooling (e.g., Jira, Confluence).

• Ability to influence and align contributors who are not direct reports.

• Demonstrated willingness and flexibility to learn new technologies and approaches; an exploratory mindset.

• Strong English communication skills (written and verbal).

Preferred Qualifications

• Advanced degree in a relevant discipline.

• Experience building generative and agentic AI solutions (e.g., orchestrating agents on top of existing services and front ends).

• Hands-on experience with MLOps/LLMOps practices and tooling.

• Front-end development experience (the emphasis of the role remains on back-end engineering).

• Experience leading a diversity of digital solution and product types.

• Pharma & Life Science commercial functional knowledge.

• Pharma & Life Science commercial data literacy; awareness of regulatory and legal considerations applicable to commercial solutions.

Non-Standard Work Schedule, Travel or Environment Requirements

• Flexibility in working hours is required to collaborate with international stakeholders across regions (e.g., Latin America, Europe, Asia — including markets such as Mexico, Brazil, Korea, and Japan). Occasional travel may be expected.

Organizational Relationships

• Chief Marketing Office (CMO)

• Global Commercial Analytics (GCA)

• International Insights & Strategy (IIS)

• Digital / AI Center of Excellence (AI CoE)

• Chief Information Security Office (CISO) / Information Security

• Pfizer Digital teams (Creation Centers, platform teams)

• External vendor and contractor delivery partners

This description indicates the general nature and level of work expected. It is not designed to cover or contain a comprehensive listing of activities or responsibilities required of the incumbent. The incumbent may be asked to perform other duties as required.

Please apply by sending your CV in English.

Work Location Assignment: Hybrid

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