Director, AI Solutions Engineering Lead

Job description

Role SummaryDo 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 Director, AI Solutions Engineering Lead, you will be the senior individual contributor anchoring the technical direction of AI products across our International portfolio — leading not a single product, but several concurrent initiatives at different stages of maturity. Recognized as an engineering expert within and beyond the organization, you will operate at the intersection of technology and product: shaping technical strategy, informing what we build and why, and remaining hands-on where it matters most — architecture decisions, reference implementations, and the hardest technical problems. This is not a people management role: you will lead through expertise, foresight, and influence — directing the technical work of blended internal and contractor teams, mentoring engineers and tech leads through defined growth structures, driving consistent ways of working across initiatives, and carrying financial accountability for engineering resourcing and industrialization. Role ResponsibilitiesOwn the technical direction of a portfolio of AI initiatives spanning multiple concurrent products at different maturity stages — from POC to MVP to enterprise-grade production — ensuring architectural coherence, component reuse, and consistent engineering quality across teams. Act as the accountable technical decision-maker for the portfolio: arbitrate cross-initiative technical conflicts, shared dependencies, and technical debt priorities; own decision outcomes and answer for them to leadership. Shape the medium-term (12–18 month) technical strategy: drive build-vs-buy assessments, vendor and platform evaluations, and feasibility input into portfolio prioritization — operating at the intersection of technical and product decision-making. Remain hands-on at the highest points of leverage: lead architecture spikes and reference implementations, take on the most complex technical problems, and use pull request review as a mechanism for setting and propagating standards. Set, evolve, and enforce engineering standards and ways of working across the portfolio; drive synchronization and consistency across blended internal and contractor teams beyond direct reporting lines. Safeguard solution quality end to end at portfolio level: testing discipline, release readiness, production reliability, and security and compliance posture, in partnership with information security and platform teams. Exercise foresight and act independently: proactively identify and drive self-initiated technical initiatives such as platform improvements, technical debt reduction programs, and engineering capability investments. Invest in people without managing them: provide structured technical mentorship to engineers and tech leads through defined growth frameworks, design documents, architecture decision records, and continuous learning practices (guilds, labs, demos); raise engineering maturity across the organization. Manage budget and spending for engineering industrialization efforts, including external contractor and vendor resources and tooling; ensure cost-effective architecture and resourcing decisions. Collaborate and influence at senior levels: partner with product owners, data science and engineering teams, leadership stakeholders, and vendor leadership; communicate complex technical concepts persuasively to technical and non-technical audiences. Challenge the status quo: evaluate emerging technologies and approaches (including generative and agentic AI), take appropriate and well-reasoned risks, and promote innovation across the portfolio. QualificationsBasic QualificationsBachelor's degree in a relevant discipline (Computer Science, Data Science, Computer Engineering, Information Systems, or a related field) and 10+ years of hands-on experience in AI, data, or software solution engineering, including building, deploying, and operating complex production systems. Recognized technical expertise with demonstrated influence beyond a single team or function; track record of settingand owning technical direction across multiple concurrent products or initiatives. Experience orchestrating delivery through blended teams of internal engineers and external vendors or contractors, holding all contributors to defined quality standards; exposure to budget or vendor-spend accountability. Deep expertise in at least one core discipline (e.g., data engineering, data science and ML engineering, AI engineering, backend/full-stack development), combined with breadth across the digital solution stack — data products, AI data products, generative/agentic AI solutions, and full-stack applications — and command of the engineering practices each maturity stage (POC, MVP, enterprise-grade) requires. Foresight and sound judgment in complex, ambiguous technical decisions; a record of acting independently on self-initiated work and of formulating strategy-level proposals and decision rationale for senior audiences. Strong, current hands-on engineering capability: Python and SQL, cloud-based analytics ecosystems (e.g., AWS, Snowflake), modern data and AI tooling, CI/CD, and MLOps/LLMOps practices. Proven ability to lead through influence: aligning engineers, teams, vendors, and senior stakeholders without direct reporting authority. Demonstrated experience mentoring and developing engineers and technical leads through structured approaches. Strong English communication skills (written and verbal). Preferred QualificationsAdvanced 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. Experience contributing to product strategy, roadmap definition, or product-led engineering teams. Experience technically 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 RequirementsFlexibility 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 RelationshipsChief Marketing Office (CMO) Global Commercial Analytics (GCA) International Insights & Strategy (IIS) Digital / AI Center of Excellence (AI CoE) AI Acceleration (AIA) program teams Chief Information Security Office (CISO) / Information Security Pfizer Digital teams (Creation Centers, platform teams) External vendor and contractor delivery partners Resources ManagedFinancial AccountabilityManages budget and spending for engineering industrialization efforts, including external contractor and vendor resources and tooling; accountable for cost-effective architecture and resourcing decisions. SupervisionIndividual contributor role with no direct reports. Provides technical supervision, mentorship, and direction across blended internal and contractor teams; identifies the need for contingent workers and directs their technical work. 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 incumben