AI Engineer

AI Engineer - Permanent - Paramus, NJ

Engineer the future of biopharma by turning cutting‑edge AI models into scalable, real‑world solutions.

Proclinical is seeking an AI Engineer to join a dynamic team in the US. The position offers an exciting opportunity to work at the intersection of technology and immuno‑oncology/pharmaceuticals, driving innovation through AI‑enabled solutions.

The successful candidate will focus on developing and deploying AI/ML models into scalable, production‑ready services while collaborating with cross‑functional teams to enhance operational decision‑making.

• Proficiency in Python for building services, pipelines, and ML tooling.
• Experience deploying AI models in production environments, including cloud platforms like AWS or Azure.
• Familiarity with big data and orchestration frameworks such as Spark and Airflow.
• Strong understanding of software engineering best practices, including CI/CD, containerization (Docker/Kubernetes), automated testing, and version control.
• Knowledge of model optimization techniques, such as ONNX Runtime, model quantization, or other performance‑oriented tools.
• Bachelor's degree or higher in Computer Science, Engineering, or a related technical field.

• Interest or experience in MLOps concepts, including model registries, feature stores, experiment tracking, and automated retraining.
• Advanced degree (Master's or higher) in a relevant field.
• Experience in regulated industries such as biopharma, healthcare, or finance.
• A portfolio showcasing launched AI/ML projects or contributions to production AI systems.
• Proficiency in SQL and familiarity with modern data warehouses like Snowflake.

• Transform AI/ML models into scalable services such as APIs, batch inference, and streaming inference with high reliability and performance standards.
• Collaborate with AI Scientists to transition research prototypes into production‑ready components, including preprocessing, feature computation, and evaluation loops.
• Integrate models with data pipelines to ensure seamless end‑to‑end workflows from raw data to AI‑driven outputs.
• Build and maintain inference pipelines using Python and orchestration frameworks like Airflow, supporting both cloud and on‑premises deployments.
• Implement CI/CD processes, containerization, and automated testing for safe and repeatable model deployments.
• Establish monitoring systems to track model performance, data drift, and system metrics, ensuring reliability and alerting for potential issues.
• Partner with business operations and commercial teams to automate workflows and create AI‑enabled services that enhance decision‑making.
• Optimize model serving for latency, throughput, and cost using strategies like caching, scaling policies, and parallelization.
• Contribute to documentation, reusable templates, and best practices to promote AI adoption across the organization.

• $ - $ Per Annum