
Machine Learning Engineer
3 weeks ago
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.
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