Agentic AI/ML Engineer for Production Systems

Pfizer; New York (NY); Hybrid; USD 163,000 - 272,000; Full time

Benefits offered by this job

401(k) with Pfizer Matching

Paid vacation

Health benefits including medical and

Paid caregiver/parental leave

Job summary

Pfizer is seeking an experienced engineer to own orchestration frameworks and agent architectures for AI-based workflows within the Innovation, Data & Analytics team. You will design prompt architecture, integrate tools and APIs, and translate analytical methodology into production-grade agents.

You will collaborate with data science, data engineering, and governance teams to ensure reliability, governance, and cost control in LLM-based systems, while advancing patient-safe, scalable solutions

Qualifications

• Independent in a fast-paced, regulated environment with cross-functional collaboration.
• 8+ years with a Bachelor's in software/ML engineering in healthcare/pharma.
• Experience with agentic frameworks and production system design/testing.
• Strong English communication and regulatory/compliance awareness.

Responsibilities

• Own orchestration frameworks and agent architecture for AI-based workflows.
• Build and harden agentic workflows for production use.
• Design prompt architecture and tool/API integrations across agentic systems.
• Translate methodology into deployed, production-grade agents.
• Implement monitoring, evaluation, and cost controls for LLM-based systems.

Skills

Software engineering

Agentic frameworks

LLM APIs

Healthcare data

Education

Bachelor's degree in CS/SE/ML

Tools

LangGraph

Pydantic AI

LLM APIs

Job description

Pfizer is seeking an experienced engineer to own orchestration frameworks and agent architectures for AI-based workflows within the Innovation, Data & Analytics team. You will design prompt architecture, integrate tools and APIs, and translate analytical methodology into production-grade agents.

You will collaborate with data science, data engineering, and governance teams to ensure reliability, governance, and cost control in LLM-based systems, while advancing patient-safe, scalable solutions