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Mid-level generalist ML/data role in metro Bangalore with broad required skills and hybrid work increases competition.
Core ML and data engineering skills are highly transferable across industries despite environmental domain context.
Many mandatory technical skills and production deployment requirements drive strict technical shortlisting filters.
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Design, build, validate, and deploy advanced machine learning models and predictive analytics solutions in environmental sectors.
Develop and maintain robust, scalable data pipelines and big data processing workflows using tools like Airflow, Spark, and Hadoop.
Contribute to software engineering best practices including modular Python coding, containerization (Docker, Kubernetes), and cloud infrastructure integration (Azure).
Proficiency in Python programming with advanced OOP features, machine learning model development, and relational database design.
Experience designing and maintaining data pipelines and big-data processing workflows using tools like Airflow, Spark, or Hadoop.
Familiarity with containerization technologies (Docker, Kubernetes), CI/CD pipelines, and cloud platforms (notably Azure).
Work Experience Required: Not explicitly mentioned in the JD.
Comfortable working in cross-functional teams embedding data-driven decision-making within strategic projects.
Experienced in developing production-quality data infrastructure and deploying machine learning models and APIs.
Skilled in balancing data science, data engineering, and software development responsibilities in complex, large-scale environments.