Manager AI & Data Architect (AI Engineer)

ROLE SUMMARY

Pfizer’s purpose is to deliver breakthroughs that change patients’ lives. Research and Development is at the heart of fulfilling Pfizer’s purpose as we work to translate advanced science and technologies into the therapies and vaccines that matter most. Whether you are in the discovery sciences, ensuring drug safety and efficacy or supporting clinical trials, you will apply cutting edge design and process development capabilities to accelerate and bring the best in class medicines to patients around the world.

Pfizer is seeking a highly skilled and motivated AI and Data Architect and hands-on AI engineering for AI-enabled data products and modern data platforms. The AI & Data Architect Manager designs and delivers cloud-scale data solutions (lake/warehouse/marts/APIs) and production AI capabilities (ML/GenAI/LLM patterns) that are reusable across the enterprise, ensuring strong engineering practices, testability, security, and operational excellence. This role demands a collaborative mindset, a passion for cutting-edge technology, and a commitment to improving patient outcomes.  The role partners across Digital and business stakeholders to deliver measurable outcomes, and mentors engineers/contractors to raise technical standards and accelerate delivery.

ROLE RESPONSIBILITIES

• Data Architecture & Platform Engineering Leadership

  • Lead data modeling and engineering across advanced data platforms to achieve digital outcomes, including solution designs for Cloud Data Lake, Data Warehouse, Data Marts, and Data APIs, with ownership of enterprise data quality standards across structured, semi‑structured, and unstructured data domains.

• Snowflake & Cloud Data Ecosystem Delivery

• • Architect and deliver scalable cloud data solutions leveraging platforms such as Snowflake and associated orchestration/automation patterns; ensure performance, maintainability, reuse across the enterprise, and support for I‑ready workloads including feature and embedding storage.

• Cloud Platform, Microservices & DevOps Architecture

  • Lead the design and implementation of cloud‑native architectures, including microservices‑based solutions, containerized workloads (e.g., Kubernetes), and DevOps practices.

  • Ensure data and AI platforms are deployable, scalable, and operable using modern CI/CD pipelines, infrastructure‑as‑code, and automated environment management

• GenAI & AI/ML Solution Architecture and Implementation

  • Lead design and implementation of AI models and algorithms (GenAI/LLM-enabled and traditional ML patterns), including model selection/orchestration, agents, RAG-style patterns, and evaluation approaches as appropriate for regulated environments.

• Engineering Excellence, CI/CD, Testing & Data Validation

  • Oversee development and execution of test plans/scripts and thorough data validation; implement automated build/test/deploy/monitoring for ETL pipelines and AI components in a CI/CD environment, including automated data quality checks and observability controls.

• Operational Reliability & Issue Resolution

  • Drive root cause analysis for production data/AI issues; implement observability, monitoring, and preventative controls to improve quality, reliability, and consistency of data products, pipelines, and vector‑based AI systems.

• Cross-Functional Delivery & Stakeholder Communication

  • Collaborate with backend engineering and other technical teams across Digital to deliver end-to-end implementations; document and present methodologies, findings, and outcomes to stakeholders, including data quality metrics and AI solution performance.

• Vendor/Contractor Technical Leadership

  • Collaborate effectively with contractors and partners to deliver technical enhancements while maintaining architecture standards, documentation quality, and delivery cadence.

  • Provides guidance and may lead/co-lead moderately complex projects.

• Continuous Improvement & Innovation

  • Stay current on AI/ML advancements and apply them to enterprise initiatives; identify reusable components and patterns that accelerate delivery across teams, including unstructured data processing and vector‑based retrieval approaches, that accelerate delivery across teams.

Education / Experience

• Bachelor’s degree (Master’s preferred) in Computer Science, Data/AI/ML, Engineering, or related field.

• 4+ years in data engineering/architecture (or equivalent), including modern data warehousing, modeling, and transformations; 2+ years delivering GenAI & AI/ML solutions in production (preferred).

Technical (Must-Have)

• Strong Python engineering (production coding, packaging, testing), plus strong SQL.

• Snowflake: data modeling, performance patterns, security concepts, and operational usage.

• Experience designing and operating cloud‑native platforms, including microservices architectures, containerization (e.g., Kubernetes), and DevOps practices such as CI/CD, automated deployments, and environment management.

• Cloud architecture for data platforms (e.g., Azure/AWS), including storage, compute, identity, and networking fundamentals.

• Building/operating ETL/ELT pipelines with CI/CD automation, monitoring, and data quality controls.

• Demonstrated experience implementing enterprise data quality standards, including validation, observability, monitoring, and lineage across structured, semi‑structured, and unstructured data.

GenAI & AI/ML (Must-Have)

• Experience delivering GenAI & AI/ML solutions end-to-end (feature/embedding generation, model integration, deployment, evaluation).

• Familiarity with LLM concepts and practices (prompting, evaluation, orchestration), and responsible AI considerations for regulated environments.

• Hands‑on experience with vector databases and embedding‑based retrieval patterns (e.g., semantic search, RAG, similarity matching).

• Experience working with unstructured and semi‑structured data (documents, text, PDFs, logs, APIs) for analytics and AI use cases.

Leadership / Ways of Working

• Proven ability to lead/co-lead moderately complex projects, mentor engineers, and guide contractors to deliver outcomes.

• Strong communication skills: can translate technical decisions and tradeoffs to non-technical stakeholders; strong documentation discipline.

PREFERRED QUALIFICATIONS

• Experience with agentic/LLM frameworks and enterprise GenAI patterns (e.g., LangChain, LlamaIndex, vector stores) and/or knowledge graph technologies.

• Experience establishing AI architecture guardrails, patterns, and reusable components across domains.

• Pharma/supply chain analytics experience (data products supporting planning, manufacturing, quality, logistics).

• Experience deploying data and AI workloads using container orchestration platforms and operating them at scale in cloud environments.

• Experience partnering with platform or infrastructure teams on cloud governance, security, and operational standards.

 NON-STANDARD WORK SCHEDULE, TRAVEL OR ENVIRONMENT REQUIREMENTS

20% travel may be required based on delivery and project priorities

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.

Information & Business Tech