Designing and operating cloud - based MLOps capabilities supporting analytical and generative AI models. Collaborating with data science and business teams for high - impact AI solutions.
About the role
- Machine Learning Engineer developing and deploying AI solutions at Welldoc for healthcare improvements. Collaborating with cross-functional teams and optimizing machine learning models in a hybrid work environment.
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.
Requirements
- Proficiency in Python and ML frameworks such as PyTorch.
- 3-5 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.
- Experience deploying models in the healthcare domain (preferred).
- Experience with feature engineering, data preprocessing, and model explainability (preferred).
- Knowledge of containerization (Docker, Kubernetes) and workflow orchestration tools (preferred).
Benefits
- generous PTO
- medical insurance
- dental insurance
- vision care
- life and disability insurance
- retirement benefits
- opportunity to participate in health savings accounts and/or dependent care accounts
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