Back
Early-mid ML role with 3–6 years and metro location increases competition, brand not Tier-1.
ML engineering requires specialized production ML and cloud skills, moderately transferable across industries.
Mandatory Master's degree, 4+ years and specific cloud/ML stack and monitoring requirements create high shortlisting strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, and deploy scalable machine learning models and production-grade ML pipelines.
Implement model monitoring and drift detection frameworks to maintain model performance in production.
Develop scalable APIs and ML services, deploy ML solutions in Microsoft Azure cloud environment, and collaborate across teams to deliver business-impacting ML systems.
Master’s degree in Computer Science, Machine Learning, Data Science, Statistics, or a related quantitative field.
4+ years of experience building and deploying machine learning models and systems in production environments.
Strong proficiency in Python, experience with ML frameworks (scikit-learn, PyTorch, TensorFlow), and building APIs using frameworks like FastAPI.
Experience working with cloud platforms, preferably Microsoft Azure, and modern data platforms such as Snowflake.
Practitioner skilled in building end-to-end production-grade ML pipelines and monitoring models post-deployment.
Experienced with distributed computing frameworks (Ray, Dask) and hyperparameter tuning tools (Optuna).
Comfortable collaborating with cross-functional teams to translate complex business problems into robust ML solutions deployed in cloud environments.