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Known analytics employer, Bangalore mid-senior ML role with strong MLOps requirements, moderately competitive.
Production ML and MLOps skills are highly transferable across industries, so low background sensitivity.
Mandatory production ML experience, Spark, Airflow, and MLOps creates high shortlisting strictness.
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Own end-to-end lifecycle of machine learning systems from problem formulation to deployment and monitoring.
Make architectural and design decisions to ensure ML systems are scalable, reliable, and efficient.
Mentor junior engineers and serve as technical point of contact managing stakeholder communications.
Bachelor's or Master's degree in Engineering, Mathematics, Statistics or related field.
Proven experience owning and deploying production ML systems end-to-end including automated training and inference pipelines.
Proficiency in Python, SQL, and hands-on experience with distributed computing frameworks (e.g., Apache Spark).
Experience with workflow orchestration tools (e.g., Airflow) and MLOps practices.
Experienced in building scalable ML systems involving classification, regression, anomaly detection, and deep learning on large datasets.
Able to handle ambiguity and complexity in design while delivering robust ML solutions in a collaborative environment.
Skilled in data engineering, MLOps, and optimizing complex data pipelines and ML models for performance.