Role & Responsibilities:
We are looking for a skilled and motivated MLOps Engineer to join our team. The ideal candidate should have at least two years of experience in data science and MLOps, a proven ability to deploy end-to-end machine learning projects, and a strong educational background from IITs, NITs, or equivalent institutions. This role combines expertise in data science, machine learning, and operationalizing models to ensure seamless integration into production systems.
Key Responsibilities:
• Work on the complete lifecycle of machine learning projects, from problem definition to deployment and monitoring.
• Develop and deploy machine learning (ML) and deep learning (DL) models to solve business problems.
• Implement MLOps pipelines for model versioning, testing, deployment, and monitoring.
• Automate workflows for data preprocessing, feature engineering, and model retraining.
• Monitor the performance of deployed models and manage updates and retraining.
• Leverage tools like Databricks, Azure ML, and cloud platforms to build scalable ML systems.
• Collaborate with cross-functional teams, including data engineers, analysts, and business stakeholders, to solve challenges.
Qualifications & Skills:
• A bachelor’s or master’s degree in computer science, Data Science, Statistics, or a related field from IITs, NITs, or similar top-tier institutions.
• 2+ years of experience in data science, machine learning, or MLOps roles.
• Proven experience deploying at least one end-to-end ML project into production.
• Proficiency in MLOps tools like MLflow, Kubeflow, Airflow, or similar.
• Hands-on experience with cloud platforms like Azure, AWS, or GCP.
• Strong expertise in ML, DL, and exploratory data analysis (EDA).
• Familiarity with CI/CD pipelines and containerization tools like Docker and Kubernetes.
• Programming proficiency in Python, R, or similar languages.
• Excellent problem-solving and analytical thinking skills.