We are seeking a skilled Software Engineer who will design, build, and maintain software systems that deliver business value. In this role, you will focus on code quality, architecture decisions, and reliable delivery while leveraging AI tools to enhance productivity. You will verify and review AI-generated code to ensure it meets our quality standards.
You will bridge the gap between building products and solving real business problems through rapid solution delivery and deep domain expertise. As part of the discovery to scale pipeline, you will identify recurring patterns and lead validated solutions generalization into enterprise capabilities.
This role encompasses organization wide technical strategy and architectural direction. You will define strategic direction, be accountable for outcomes, shape organizational practices, and maintain external industry visibility. You will handle organizational scale complexity, creating clarity from chaos.
ROLE RESPONSIBILITIES DELIVERY
- Application Deployment: Shape deployment strategy across the business unit, drive platform adoption, establish deployment best practices at scale, and demonstrate deployment architecture expertise.
- Data Integration: Define data integration patterns and best practices, architect large scale data flows, solve complex integration challenges, and act as the authority on enterprise data integration.
- Full Stack Development: Work comfortably in any language, rapidly acquire new skills, deliver production solutions in days, shape full stack practices, and exemplify polymathic engineering.
- Problem Discovery: Shape approaches to problem discovery, transform ambiguous situations into clear opportunities, influence how teams engage with business problems, and become the go to person for undefined challenges.
- Prototype to Production: Shape prototype to production practices, define standards for handoff, establish quality gates for each conversion layer, and efficiently turn validated prototypes into production systems.
ARTIFICIAL INTELLIGENCE
- AI Engineering Strategy: Pioneer AI integration patterns, publish on AI augmented development, and lead AI in engineering.
- AI Evaluation & Observability: Shape AI observability and evaluation practices, pioneer novel evaluation methodologies, contribute to industry standards for LLM evaluation, and lead AI quality assurance at scale.
- AI Literacy: Shape AI literacy programs, contribute to industry understanding, speak publicly on AI applications, and lead AI thought leadership.
- AI Augmented Development: Shape AI development tooling strategy, pioneer new patterns, and balance speed with verification rigor.
- LLM Integration: Shape LLM integration strategy, architect multi provider gateway patterns, drive enterprise LLM governance, and lead LLM integration expertise.
DOCUMENTATION
- Knowledge Strategy: Enable broad knowledge sharing and achieve documentation excellence.
- Developer Experience: Create innovative approaches to internal platforms, build high adoption self service capabilities, and influence industry practices.
- Knowledge Management: Build knowledge sharing organizations, drive learning culture, and provide governance frameworks.
- Pattern Generalization: Define organizational approach to pattern generalization, balance standardization with field flexibility, and build platform capabilities from field discoveries.
- Technical Writing: Innovate documentation approaches, maintain exceptional clarity, and lead documentation initiatives.
MACHINE LEARNING
- ML Strategy: Lead enterprise ML initiatives and innovate on ML approaches across the business unit.
- Model Development: Innovate on ML for novel problems, shape strategy, and contribute to research or publication.
- Model Fine Tuning: Innovate fine tuning approaches, shape customization strategy, and serve as a fine tuning authority.
- Retrieval Augmentation: Innovate on RAG architectures, shape strategy, and contribute to research or tooling.
BUSINESS
- Technology Driven Strategy: Represent the technical perspective at executive level and bridge engineering and business.
- Business Immersion: Influence domain strategy, embody the immersion mindset, and shape engineering engagement with business.
- Stakeholder Management: Shape stakeholder practices, manage executive relationships, and build exceptional partnerships.
PEOPLE
- Technical Leadership: Develop leaders, shape talent strategy, and build high performing teams.
- Multi Audience Communication: Represent engineering externally, communicate across all levels, and bridge audience gaps.
- Team Collaboration: Cultivate collaborative culture, transform dysfunctional dynamics, and mentor collaboration excellence.
ROLE BEHAVIOURS
- Curiosity: Shape culture around continuous learning and experimental initiatives.
- Outcome Ownership: Define accountability standards and drive transformational initiatives.
- Polymath Orientation: Champion cross disciplinary learning and holistic solutions.
- Precision Communication: Mentor precise communication and drive clarity across roles.
- Systems Thinking: Shape system design practices, conduct chaos engineering, and bridge technical systems with business processes.
QUALIFICATIONS
Bachelor's degree in Computer Science, Engineering, or related field with extensive relevant experience. Deep technical expertise with a proven track record of organization wide technical leadership.
EXPERT LEVEL SKILLS
- Business Immersion: Recognized thought leader shaping strategy and engagement.
- Data Integration: Define patterns, architect large scale flows, and lead enterprise integration.
- Full Stack Development: Deliver production solutions quickly, shape practices, and exemplify polymathy.
- Multi Audience Communication: Shape communication practices, represent externally, and bridge audiences.
- Problem Discovery: Transform ambiguous situations, guide business problem engagement, and lead undefined challenges.
- Rapid Prototyping & Validation: Shape rapid validation culture, define prototype to production standards, and deliver in days.
- Stakeholder Management: Manage executive relationships and build exceptional partnerships.
- Team Collaboration: Build collaborative culture and mentor collaboration excellence.
PRACTITIONER LEVEL SKILLS
- AI Evaluation & Verification: Lead governance, create evaluation standards, and train on AI verification.
- AI Literacy: Guide adoption decisions, assess risks, and train responsible use.
- AI Augmented Development: Optimize tool usage, train on workflows, evaluate new tools, and balance speed with verification.
- Architecture & Design: Design complex systems, evaluate options, guide decisions, and mentor architecture.
- Code Quality & Review: Enforce standards, mentor code review, ensure AI verification depth, and drive testing strategies.
- Developer Experience: Lead initiatives, design self service platforms, establish metrics, conduct research, and mentor platform thinking.
- Knowledge Management: Lead initiatives, create knowledge systems, and mentor sharing.
- Pattern Generalization: Lead initiatives, define criteria, and mentor abstraction design.
- Service Management: Lead practices, optimize portfolios, balance investments, and train service thinking.
- Site Reliability Engineering: Define standards, drive incident improvements, design capacity, and mentor SRE practices.
- Technical Writing: Define standards, create systems and templates, train on spec driven development, and ensure quality.
WORKING LEVEL SKILLS
- Cloud Platforms: Design solutions, manage infrastructure as code, implement security, and troubleshoot.
- Data Modeling: Create efficient models, optimize queries, handle migrations, and choose storage.
- DevOps & CI/CD: Build pipelines, manage infrastructure as code, implement monitoring, and design deployment strategies.
- Lean Thinking & Flow: Optimize processes, measure cycle time, remove bottlenecks, and maintain quality.
- Technical Debt Management: Prioritize systematically, balance debt and features, and make pragmatic trade offs.
- Time Management & Deep Work: Protect focused time, balance priorities, minimize context switching, and help others understand capacity.
Work Location Assignment: Hybrid
We believe that a diverse and inclusive workforce is crucial to building a successful business. As an employer, Pfizer is committed to celebrating diversity in all its forms, allowing us to be as diverse as the patients and communities we serve.
We are proud to be a disability inclusive employer, ensuring equal employment opportunities for all candidates. We encourage you to apply with confidence, knowing we will make reasonable adjustments to support your application and future career.
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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.