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Mid-level generalist data role, metro location, and broad skills create high applicant competition.
Data engineering and Python/SQL skills transfer easily across industries.
Explicit 3–5 years requirement plus domain and tool expectations enforce moderate filtering.
Design, build, and operate AI and data systems including scalable data pipelines and AI/ML solutions supporting operational efficiency and decision-making.
Develop, test, and deploy AI applications including LLM-based workflows and predictive automation, supporting model performance optimization.
Contribute to CI/CD practices, cloud deployment (Azure, Databricks), and support monitoring and troubleshooting of data pipelines and AI systems.
3 to 5 years of experience in data engineering, software engineering, or AI/ML engineering with cloud environment exposure.
Proficiency in SQL and Python for data processing and transformation; experience with data pipeline concepts including batch and streaming.
Familiarity with modern data platforms/tools like Databricks, dbt, Kafka, or similar; basic understanding of data modeling and architecture patterns.
Experience or exposure to AI/ML concepts and tools, LLM-based solution integration, and cloud platforms such as Azure, AWS, or GCP.
Engineer with solid foundation in software and data engineering aiming to deepen experience in modern data platforms and applied AI.
Practitioner comfortable working within established engineering standards and collaborating closely with senior engineers and cross-functional teams.
Candidate interested in operationalizing AI and data pipelines with focus on production readiness, performance monitoring, and continuous improvement.