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Recognizable Tier-2 brand, mid-level data role and 5+ years requirement produce moderate candidate competition.
Core data engineering and automation skills are transferable across industries, though AI-agent specialization adds moderate specificity.
Explicit 5+ years plus multiple mandatory technologies and cloud/orchestration skills raise shortlisting strictness.
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Automate and refactor Python/PySpark data workflows into scalable, production-ready systems in collaboration with data scientists.
Design, implement, and manage AI-driven automated data pipelines to optimize processes and enable real-time decision-making.
Build and integrate automation tools including job schedulers, version control, and monitoring solutions for data science projects.
5+ years of relevant experience in data science engineering or automation.
Proficiency in Python scripting, automation, and modular design.
Experience with AI agents involving reinforcement learning, decision trees, autonomous or multi-agent systems.
Experience with Apache Airflow or other workflow orchestration tools; strong Git version control skills; cloud deployment experience on AWS, GCP, or Azure; familiarity with Docker, Kubernetes, and API deployment frameworks like FastAPI or Flask.
Experienced in transforming analytical notebooks into reliable, maintainable production pipelines collaborating closely with data scientists.
Skilled in applying advanced AI techniques to automate and optimize complex workflows in data-driven environments.
Comfortable working with cloud-native technologies and automation frameworks to deliver scalable and efficient data operations.