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Senior, niche ML-data role in Bangalore reduces mid-level applicant competition.
Highly specialized ML and big-data engineering skills limit cross-industry transferability.
Explicit 12+ years and many mandatory ML-data engineering and big-data tech stack requirements.
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Design, build, and optimize scalable batch and streaming data pipelines using distributed frameworks like Spark, Databricks, and Kafka.
Lead design and development of robust data models, feature pipelines, and ETL/ELT frameworks focused on analytics and machine learning workflows, ensuring data quality, observability, lineage, and performance.
Build and operationalize end-to-end ML models including feature engineering, training, evaluation, deployment, CI/CD, model versioning, monitoring, and automation in partnership with data scientists.
12+ years of experience in ML-Data Engineering development.
Strong skills in SQL/NoSQL, Python, PySpark, ML lifecycle frameworks (MLflow, Spark-ml), and orchestration tools such as Airflow, Oozie, or Dagster.
Expertise in big data modeling, distributed data processing, and lake & warehouse architectures at large operational scale.
Hands-on experience with Unix, Hadoop, containerization (Docker, Kubernetes), ML lifecycle tools, and basic understanding of foundational modeling concepts like regression, classification, and statistical models.
Senior-level engineer with deep expertise in both data engineering and machine learning operationalization at scale, capable of end-to-end ownership.
Experienced in designing data architectures and pipelines that emphasize data quality, observability, and performance in distributed environments.
Comfortable working closely with data science and product teams to translate prototypes into production-grade, scalable ML solutions and implement automation in workflows.