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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.
Job Responsibility:
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
Requirements:
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