AI/ML Engineer

About the Role

We’re building out an AI function to advance machine learning capabilities across manufacturing, quality, and release operations. We’re looking for a contract AI/ML Engineer who has built and taken ML models to production. You’ll join a small, growing team with an ambitious mandate: build and scale AI/ML initiatives across the organization, and help shape the framework and MLOps practices behind them.

Key Responsibilities

• Own AI/ML initiatives end-to-end, from model development through production deployment
• Build, deploy, and operate ML models and AI-driven workflows in production
• Develop and extend our AI/ML framework to support new initiatives as they emerge
• Implement monitoring, retraining, and model lifecycle management practices suited to a regulated (GxP) environment
• Work extensively with Snowflake as the core data and ML platform (Snowpark ML, Cortex, model registry), identifying gaps as new needs arise
• Partner with AI leadership to prioritize and shape a growing pipeline of AI/ML initiatives

Required Qualifications

AI/ML in Production

• 5+ years in data science/ML engineering, with demonstrable experience deploying and operating models in production (not just prototyping)
• Strong Python skills with the standard ML stack: scikit-learn, XGBoost/LightGBM, pandas, and at least one deep learning framework (PyTorch or TensorFlow)
• Hands-on MLOps experience (MLflow, Kubeflow, SageMaker/Azure ML): experiment tracking, model registry, deployment, monitoring, drift detection, retraining
• CI/CD for ML (Git, GitHub Actions/Azure DevOps/Jenkins) and containerization with Docker (Kubernetes a plus)
• Experience serving models in production: REST/gRPC APIs (FastAPI or similar), batch scoring pipelines, model versioning
• Model monitoring and observability: data/concept drift, performance degradation, alerting (Evidently, Arize, or platform-native tools)
• Workflow orchestration experience (Airflow, Prefect, Dagster, or Snowflake Tasks)
• Experience building or extending reusable ML frameworks used across multiple initiatives
• Document AI / NLP experience for unstructured records: OCR, entity extraction, text classification

Data & Platforms

• Hands-on experience with Snowflake as a data and ML platform (Snowpark, Cortex, or deploying models against Snowflake data)
• Solid SQL and data engineering fundamentals: pipelines, data quality, integration with enterprise source systems
• Cloud experience (AWS, Azure, or GCP) for AI/ML workloads
• Comfortable presenting technical approaches and trade-offs to stakeholders across engineering, quality, and business functions

Preferred Qualifications

• Experience in pharma/biotech manufacturing or regulated environments: quality systems (QMS), MES data, GxP, and computer system validation (CSV)
• R&D/Labs exposure: drug discovery, lab automation, or scientific data workflows
• Generative AI and LLM experience: RAG pipelines, document intelligence, agentic workflows

What We Offer

• Competitive compensation
• The opportunity to join an early-stage AI function at a global life sciences manufacturer
• Direct impact on how life-saving therapies are manufactured and released
• Hands-on work in a fast-moving, collaborative environment

Pay: $100.00 - $125.00 per hour

Education:

• Master’s (Preferred)

Experience:

• MLOps: 3 years (Preferred)

Willingness to travel:

• 25% (Required)

Work Location: Hybrid remote in Bridgewater, NJ 08807