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Tier-1 employer, mid-level generalist role, and metro location increase applicant competition.
Core data engineering and MLOps skills are transferable across industries but require domain-specific experience.
Explicit 5–6 years plus mandatory ML, PySpark, cloud, and MLOps skills makes filters highly stringent.
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Design, develop, and maintain scalable data pipelines and cloud-based data platforms to support AI/ML workloads.
Build, deploy, optimize, and operationalize machine learning models at enterprise scale, including full ML lifecycle management and CI/CD implementation.
Collaborate with cross-functional teams to ensure data quality, system scalability, and reliable AI/ML platform performance.
5–6 years of experience in Data Engineering, Machine Learning Engineering, or AI-related roles.
Strong programming skills in Python, expert-level SQL including data modeling and performance tuning, and hands-on PySpark experience.
Hands-on experience with cloud platforms (AWS, Azure, or GCP) and MLOps practices including CI/CD pipeline development.
Solid understanding of AI/ML concepts including Generative AI, LLMs, prompt engineering, and model lifecycle management.
Experienced in enterprise-scale deployment and monitoring of ML/AI solutions, integrating these into data platforms and business applications.
Skilled in building scalable ETL/ELT pipelines and cloud-native, containerized architectures with DevOps practices.
Collaborates effectively with data scientists, engineers, and business stakeholders in Agile/Scrum environments.