Job
Description
We are seeking a skilled and experienced Machine Learning Engineer to join our team.The ideal candidate will have a strong background in Python and PyTorch, along with 4-8 years of experience deploying ML/AI models to production. This role requires excellent analytics skills and a good working knowledge of Databricks. You will work closely with data scientists, clinicians, software engineers, and product teams to design, build, and optimize scalable machine learning solutions. Role & responsibilities Develop, train, and optimize machine learning models using PyTorch and other ML frameworks. Deploy and maintain ML models in production environments, ensuring scalability, performance, and reliability. Utilize Databricks for data processing, model training, model deployment, and pipeline optimization. Deploy Retrieval-Augmented Generation (RAG) pipelines to production for improved AI-driven applications. Collaborate with data engineers to design and implement ETL workflows and data pipelines. Perform rigorous testing, validation, and monitoring of deployed models. Optimize model inference for low latency and high throughput applications. Work with stakeholders to translate business problems into ML solutions. Stay up to date with the latest advancements in machine learning, deep learning, and AI deployment strategies. Preferred candidate profile Proficiency in Python and ML frameworks such as PyTorch. 4-8 years of experience deploying machine learning models to production. Knowledge of MLflow for experiment tracking and model management. Strong experience with Databricks for ML development and deployment. Hands-on experience with MLOps, CI/CD pipelines, and cloud-based deployment (AWS, Azure, or GCP). Solid understanding of data structures, algorithms, and software engineering principles. Experience working with large-scale datasets and distributed computing frameworks. Experience with deploying Retrieval-Augmented Generation (RAG) pipelines to production. Excellent analytical and problem-solving skills. Strong communication skills and ability to work in a collaborative team environment. Preferred Qualifications Experience deploying models in the healthcare domain. Experience with feature engineering, data preprocessing, and model explainability. Knowledge of containerization (Docker, Kubernetes) and workflow orchestration tools Familiarity with LLMs, NLP, or reinforcement learning is a plus.
