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. Primary Responsibilities 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. Skills & Requirements 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. Preferred Qualifications 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. The AI Engineer's responsibilities will be: 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. Compensation $110,000.00 - $125,000.00 Per Annum #J-18808-Ljbffr