Interested in this AI/ML Engineer role at Pfizer?
Apply Now →Skills & Technologies
About This Role
ROLE SUMMARY
The Head of AI \& Agentic Platform Engineering owns the infrastructure layer that makes Pfizer's AI ambitions executable, the compute, LLM gateway, MLOps machinery, and observability platform on which every AI workload at Pfizer runs. This is not a supporting function. It is the capability that determines whether Pfizer's AI strategy moves at the speed of ambition or the speed of infrastructure constraints. The platform this team builds is the difference between a data scientist who spends two weeks provisioning an environment and one who is running experiments on day one, and between an AI model that takes six months to reach production and one that ships in days through a governed, automated deployment pipeline.
The scope of AI workloads this platform must support is broad. Each Pfizer domain (i.e., R\&D, Commercial, Global Supply, Enabling Functions) has distinct compute, latency, governance, and reliability requirements, and this platform must serve all of them without compromise. As Pfizer advances from assistive AI tools toward autonomous agentic systems that take multi\-step actions across the enterprise, the demands on this platform will grow in both complexity and consequence. The LLM gateway, agent orchestration layer, and observability infrastructure this leader builds today must be architected for that future from the outset.
The team of engineers is organized across four pods, LLM Gateway \& Model Serving, Compute \& Environments, Runtime Enablement and Registry, Deploy \& Trust, each owning a distinct and critical layer of the AI infrastructure stack including agent lifecycle management.
ROLE RESPONSIBILITIES
Gateway \& Serving
- Enterprise LLM gateway, access control, multi\-model routing, rate limiting, cost attribution, and audit logging for all LLM interactions across Pfizer, including agentic AI workloads.
- Model serving infrastructure, low\-latency inference, auto\-scaling, and multi\-region deployment for production models.
- Agentic AI runtime, the infrastructure layer that supports autonomous AI agents taking multi\-step actions across Pfizer's systems. This is meaningfully different from stateless LLM inference: agents require stateful process management, short\-term and long\-term memory, tool\-calling orchestration, and the ability to coordinate with other agents. As Pfizer's agentic AI portfolio grows, this layer becomes one of the most strategically critical components of the platform. The Head of AI \& Agentic Platform Engineering is expected to architect this capability proactively, not wait for agent use cases to arrive and then retrofit the infrastructure.
- Gateway observability, real\-time usage monitoring, cost attribution by team and use case, and anomaly detection. Enterprise tool and MCP registry, the governed catalog of tools, APIs, and data sources that AI agents are permitted to call at runtime. As the number of agent\-callable tools grows across Pfizer, this registry becomes the mechanism by which the platform enforces what agents can do, not just what they can say. Built and maintained in close partnership with the Trusted AI team's agent governance function.
Compute \& Environments
- Enterprise compute provisioning, GPU, TPU, and CPU infrastructure across cloud and on\-premises, including capacity planning, FinOps governance, and utilization optimization.
- Pre\-configured AI environments, reproducible, governed workspaces that enable data scientists to focus on scientific problems, not infrastructure.
- Infrastructure as Code, automated, auditable environment provisioning across development, staging, and production.
- HPC support, infrastructure capable of supporting large\-scale scientific simulation and molecular modeling workloads (preferred, not required).
Runtime Enablement
- MLOps platform, experiment tracking, model versioning, automated evaluation, deployment pipelines, and model registry, with integration into Trusted AI's risk classification and sign\-off process.
- Production observability, monitoring, alerting, and dashboarding for AI systems in production: latency, throughput, drift detection, and model health.
- Developer experience, APIs, SDKs, and documentation that enable federated teams to deploy production models without deep infrastructure expertise.
Registry, Deploy \& Trust
This pod was previously a standalone Trust Engineering team. Its integration into AI \& Agentic Platform Engineering reflects a deliberate architectural decision: agent lifecycle management, register, deploy, monitor, govern, retire, is infrastructure, and the operational boundary between deploying a model and operating an agent has collapsed. The pod owns:
- Enterprise AI model registry, the authoritative record of every AI model and agent in development, staging, and production across Pfizer, including metadata, version history, risk tier, Trusted AI validation status, ownership, and complete audit trail.
- Deployment pipeline infrastructure, automated pipelines through which models and agents move from development to staging to production, with Trusted AI sign\-off gates enforced as first\-class pipeline steps. Includes release management, canary deployments, A/B testing, and rapid rollback capabilities.
- Production monitoring and drift detection, continuous observation of AI system performance in production: prediction quality, output distributions, latency, throughput, and drift. For agentic systems, monitoring extends to agent behavior, action sequences, tool usage, decision consistency, and anomalous behavior detection.
- Guardrails and policy enforcement, the technical implementation of Trusted AI's governance policies as executable runtime controls: input/output filtering, PII detection, agent action controls, permission scoping, circuit breakers, and prompt injection defenses designed in partnership with the CISO organization.
- GxP\-compliant audit trail, complete, tamper\-evident logging of every deployment event, configuration change, and model transition, meeting the documentation standards required for AI systems operating in regulated pharmaceutical environments.
BASIC QUALIFICATIONS
- 12\+ years in software or infrastructure engineering, with 7\+ years in AI/ML platform, MLOps, or AI infrastructure roles at significant scale.
- Demonstrated experience building and operating multi\-tenant AI/ML platform infrastructure, compute provisioning, model training pipelines, model serving, and production monitoring.
- Deep hands\-on experience with LLM gateway or model serving infrastructure, multi\-model routing, inference optimization, access control, and cost attribution at enterprise scale.
- Proven MLOps platform experience with documented outcomes in deployment velocity, reliability, and developer satisfaction.
- Strong IaC practices in a multi\-cloud architecture (Azure, AWS, GCP including Terraform expertise.
- Experience leading platform teams with an SLA\-driven, product\-minded operating model.
- Demonstrated ability to collaborate across organizational boundaries, with adjacent platform teams, security functions, and governance stakeholders.
- Ability to translate infrastructure architecture and trade\-offs for both technical teams and senior business stakeholders.
- Experience with encryption and security tools, techniques, and best practices.
- Experience operating AI infrastructure in a regulated environment with GxP controls, audit trail requirements, and validated environment obligations.
- Candidate demonstrates a breadth of diverse leadership experiences and capabilities including: the ability to influence and collaborate with peers, develop and coach others, oversee and guide the work of other colleagues to achieve meaningful outcomes and create business impact.
PRFERRED QUALIFICATIONS
- Experience building or operating ML platform infrastructure at a major technology company (Google, Meta, Microsoft, OpenAI, or equivalent) at petabyte scale with thousands of concurrent ML engineers.
- Experience designing agentic AI infrastructure, specifically the orchestration layer, memory architecture (short\-term context, long\-term persistent memory), tool\-calling and MCP integration, agent\-to\-agent communication, and the safety architecture required to constrain autonomous agents operating in production. Candidates who have built or operated agent runtimes at scale, whether in a research or product context, will be strongly preferred.
- Deep LLM\-specific infrastructure experience: KV cache management, speculative decoding, quantization trade\-offs, and concurrent multi\-model serving.
- HPC environment experience, job schedulers (SLURM, LSF, or equivalent), parallel file systems, and large\-scale scientific compute workloads.
Other Job Details:
- Last Day to Apply: 7/28/2026
- Work Location Assignment: Hybrid
The annual base salary for this position ranges from $300,100\.00 to $500,100\.00\.\* In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 30\.0% of the base salary and eligibility to participate in our share based long term incentive program. We offer comprehensive and generous benefits and programs to help our colleagues lead healthy lives and to support each of life’s moments. Benefits offered include a 401(k) plan with Pfizer Matching Contributions and an additional Pfizer Retirement Savings Contribution, paid vacation, holiday and personal days, paid caregiver/parental and medical leave, and health benefits to include medical, prescription drug, dental and vision coverage. Learn more at Pfizer Candidate Site – U.S. Benefits \| (uscandidates.mypfizerbenefits.com). Pfizer compensation structures and benefit packages are aligned based on the location of hire. The United States salary range provided does not apply to Tampa, FL or any location outside of the United States. \* The annual base salary for this position in Tampa, FL ranges from $270,100\.00 to $450,100\.00\. This role is posted in multiple locations. If you are applying for the role in an secondary job posting location where pay transparency regulations apply, your Talent Advisor will share the local pay information with you during the first interview.
Relocation assistance may be available based on business needs and/or eligibility.
Candidates must be authorized to be employed in the U.S. by any employer.
U.S. work visa sponsorship (such as TN, O\-1, H\-1B, etc.) is not available for this role now or in the future.
Sunshine Act
Pfizer reports payments and other transfers of value to health care providers as required by federal and state transparency laws and implementing regulations. These laws and regulations require Pfizer to provide government agencies with information such as a health care provider’s name, address and the type of payments or other value received, generally for public disclosure. Subject to further legal review and statutory or regulatory clarification, which Pfizer intends to pursue, reimbursement of recruiting expenses for licensed physicians may constitute a reportable transfer of value under the federal transparency law commonly known as the Sunshine Act. Therefore, if you are a licensed physician who incurs recruiting expenses as a result of interviewing with Pfizer that we pay or reimburse, your name, address and the amount of payments made currently will be reported to the government. If you have questions regarding this matter, please do not hesitate to contact your Talent Acquisition representative.
EEO \& Employment Eligibility
Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, disability or veteran status. Pfizer also complies with all applicable national, state and local laws governing nondiscrimination in employment as well as work authorization and employment eligibility verification requirements of the Immigration and Nationality Act and IRCA. Pfizer is an E\-Verify employer. This position requires permanent work authorization in the United States.
Pfizer endeavors to make www.pfizer.com/careers accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process and/or interviewing, please email [email protected]. This is to be used solely for accommodation requests with respect to the accessibility of our website, online application process and/or interviewing. Requests for any other reason will not be returned.
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.
Information \& Business Tech
Salary Context
This $270K-$500K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Pfizer, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($385K) sits 76% above the category median. Disclosed range: $270K to $500K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Pfizer AI Hiring
Pfizer has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $271K - $500K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.