ML Ops & Observability Engineer

Use Your Power for Purpose

At Pfizer, technology drives everything we do. You will play a pivotal role in implementing impactful and innovative technology solutions across all functions, from research to manufacturing. Whether you are digitizing drug discovery and development, identifying innovative solutions, or streamlining our processes, you will be making a significant impact on countless lives. 

What You Will Achieve

MLOps Platform Execution & Model Operations

• Lead the design, implementation, and operation of MLOps platforms supporting model development, deployment, monitoring, and lifecycle management.

  
  
  

• Own production workflows for:

  
  
  

  • Model packaging and deployment

    
    
    
    

  • Versioning and rollback

    
    
    
    

  • Promotion across environments (dev/test/prod)

    
    
    

• Implement standardized CI/CD pipelines for ML workloads, integrating with enterprise DevOps and infrastructure platforms.

  
  
  
  

• Partner with infrastructure and DataOps teams to ensure ML workloads run on secure, scalable, and cost-effective cloud-native environments (AWS/Azure).

  
  
  

  • Translate Director-level AI platform strategy into reliable, repeatable ML operational capabilities.

    
    
    

Model, Data & System Observability

• Own end-to-end observability for ML systems, spanning:

  
  
  

  • Model performance and behavior

    
    
    

  • Data quality and drift

    
    
    

  • Pipeline health and system reliability

    
    
    
    

• Implement and operate observability tooling using:

  
  
  

• • OpenTelemetry for distributed tracing

    
    
    

  • Metrics and dashboards (Prometheus, Grafana)

    
    
    
    

  • Logs and analytics (ELK or equivalent)

    
    
    

• Define and track ML-specific reliability signals, such as:

  
  
  

  • Model performance degradation

    
    
    
    

  • Data drift and feature anomalies

    
    
    

  • Prediction latency and failure rates

    
    
    

• Establish SLOs and alerting strategies for ML services in production.

  
  
  

Testing, Validation & Responsible AI Enablement

• Ensure testing and validation are embedded throughout the ML lifecycle, including:

  
  
  

  • Model validation and regression testing

    
    
    
    

  • Data and feature consistency checks

    
    
    
    

  • Deployment verification and rollback testing

    
    
    

• Integrate automated ML testing and quality gates into CI/CD pipelines.

  
  
  

• Support non-functional testing for ML systems, including:

  
  
  
  

  • Performance and scalability testing

    
    
    

  • Reliability and resilience testing

    
    
    
    

  • Security and access validation

    
    
    

• Partner with AI, data, and compliance teams to support responsible and compliant AI operations, including auditability, traceability, and explainability hooks (where required).

  
  
  

AI Platform Enablement & Cross‑Team Collaboration

• Enable data scientists and ML engineers to move models from experimentation to production efficiently and safely.

  
  
  

• Provide reusable tooling, templates, and paved paths for:

  
  
  

  • Experiment tracking

    
    
    

  • Model registry usage

    
    
    

  • Deployment and monitoring patterns

    
    
    

• Collaborate closely with:

  
  
  

  • Infrastructure Engineering (runtime, scaling, security)

    
    
    

  • DataOps Engineering (data pipelines, feature stores, data quality)

    
    
    
    

  • Product and analytics leaders to align ML capabilities to business outcomes.

    
    
    

Reliability, Incident Management & Continuous Improvement

• Own operational reliability for ML platforms and services.

  
  
  

• Lead response to ML-related production incidents, including:

  
  
  

  • Model failures or degradations

    
    
    

  • Data drift–driven issues

    
    
    

  • Pipeline or inference outages

    
    
    

• Conduct post-incident reviews and drive systemic improvements.

  
  
  

• Continuously improve MLOps maturity using SRE-inspired practices and metrics.

  
  
  

People Leadership & Engineering Ways of Working

• Set clear expectations for operational ownership, quality, and delivery.

  
  
  

• Coach engineers on:

  
  
  

  • MLOps best practices

    
    
    

  • Observability and reliability mindset

    
    
    

  • Secure and compliant AI operations

    
    
    

• Establish strong engineering discipline through design reviews, runbooks, documentation, and continuous learning.

  
  
  

• Act as the primary execution partner to the Director-level Commercial AI Analytics Solutions & Engineering Lead for ML operations and observability.

  
  
  

Here Is What You Need (Minimum Requirements)

• 8+ years of experience in ML engineering, MLOps, platform engineering, or related roles, with 3+ years of people leadership.

  
  
  
  

• Strong hands-on experience operationalizing ML systems in AWS or Azure environments.

  
  
  
  

• Proven expertise in:

  
  
  
  

  • MLOps pipelines and tooling (experiment tracking, model registry, deployment, monitoring)

    
    
    
    

  • CI/CD for ML workloads (e.g., GitHub Actions or equivalent)

    
    
    
    

  • Containerized and cloud-native ML runtimes

    
    
    
    

• Solid understanding of testing and validation for ML systems, including:

  
  
  
  

  • Model regression and performance testing

    
    
    
    

  • Data and feature validation

    
    
    
    

  • Deployment and rollback verification

    
    
    
    

• Strong experience implementing observability and reliability practices using tools such as OpenTelemetry, Prometheus, Grafana, and ELK.

  
  
  
  

• Demonstrated experience with DevSecOps and secure SDLC for AI/ML systems, including secrets management and access controls.

  
  
  
  

• Proficiency in programming and scripting (e.g., Python, Bash, SQL; familiarity with ML frameworks).

  
  
  
  

• Strong communication and collaboration skills; ability to deliver outcomes through teams and influence cross-functionally.

  
  
  

Bonus Points If You Have (Preferred Requirements)

• Master's degree in Computer Science, Data Science, AI/ML, or related field.

  
  
  
  

• Experience with MLOps platforms and tools (e.g., MLflow, Kubeflow, feature stores).

  
  
  
  

• Background in data drift detection, model monitoring, and ML reliability engineering.

  
  
  
  

• Familiarity with responsible AI, governance, or regulated environments.

  
  
  
  

• Relevant certifications:

  
  
  
  

  • AWS/Azure Professional

    
    
    

    o   Kubernetes (CKA/CKAD)

    
    
    
    

• Cloud security or data/AI platform certifications

  
  
  

Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.