We are seeking a Senior Data Engineer with a strong background in software development and machine learning engineering to design, build, and optimize scalable data pipelines and infrastructure. This role is critical in enabling advanced analytics, AI/ML initiatives, and data-driven decision-making across the organization. You will work closely with data scientists, ML engineers, and software developers to deliver production-grade data products and intelligent systems. Key Responsibilities: • Design and build robust, scalable, and efficient data pipelines to support analytics and machine learning workflows. • Develop and maintain data models, ETL/ELT processes, and data lake/data warehouse architectures. • Collaborate with ML engineers to productionize machine learning models, ensuring efficient integration with data infrastructure. • Build and maintain CI/CD pipelines for data and ML workflows, ensuring automation and reproducibility. • Write clean, modular, and testable code using software engineering best practices (e.g., version control, code reviews, automated testing). • Monitor data systems for reliability, performance, and data quality; implement monitoring and alerting solutions. • Work cross-functionally with data scientists, analysts, and product teams to understand data needs and deliver solutions. • Contribute to architectural decisions, technology evaluations, and mentoring junior engineers. Required Qualifications: • Bachelor's or Master's degree in Computer Science, Engineering, or a related field. • Familiar with LLM and its ecosystem. • 5+ years of experience in data engineering, software engineering, or ML engineering roles. • Proficiency in programming languages such as Python, Java, or Scala. • Experience with distributed data processing frameworks such as Spark, Flink, or Beam. • Deep knowledge of data warehousing technologies (e.g., Snowflake, BigQuery, Redshift) and SQL. • Familiarity with cloud platforms (AWS, GCP, or Azure) and tools like Airflow, dbt, Terraform. • Solid understanding of machine learning workflows, including model training, versioning, deployment, and monitoring. • Experience with software development practices: Git, unit testing, CI/CD, containerization (Docker, Kubernetes). • Strong written and verbal communication skills.