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: $75.00 - $125.00 per hour
Education:
Experience:
- MLOps: 3 years (Preferred)
Willingness to travel:
Work Location: Hybrid remote in Bridgewater, NJ 08807
