Overview

Summary Are you passionate about building and operationalizing machine learning systems at scale?

Responsibilities

• Design, build, and manage end-to-end machine learning systems, including data ingestion, training pipelines, deployment, monitoring, and retraining workflows.
• Develop and deploy batch and real-time inference services using REST APIs and scalable cloud-native architectures.
• Package, migrate, and optimize ML solutions across environments, including Databricks and AWS-based deployments.
• Build and maintain CI/CD pipelines for ML workloads with robust version control, testing, rollback mechanisms, and environment isolation.
• Monitor production models and services, implement drift detection frameworks, and drive model retraining and redeployment.
• Collaborate with Data Scientists and Engineers to develop, deploy, and maintain machine learning solutions and data-driven applications.
• Support GenAI and LLM workloads, including model serving, inference optimization, and cost-performance management.

Qualifications

• 2–4 years of hands-on experience in Software Engineering, Data Engineering, ML Engineering, or MLOps.
• Strong proficiency in Python with experience developing clean, production-grade code.
• Hands-on experience with AWS services such as EC2, S3, and Lambda.
• Working knowledge of SQL, PySpark, and exposure to Databricks or equivalent data platforms.
• Familiarity with MLOps tools such as MLflow, Model Registries, ML Observability, and Drift Detection solutions.
• Opportunity to work on enterprise-scale AI, ML, and Generative AI initiatives.
• Exposure to modern cloud-native architectures and advanced MLOps frameworks.
• Collaborative environment with highly skilled Data Scientists and Engineering teams.