Senior MLOPS Engineer

As a Senior MLOps Engineer, your role involves developing scalable and maintainable Python-based backend systems to support ML workflows. You will be responsible for building and optimizing APIs and services using frameworks like Flask and Django. Collaboration with cross-functional teams such as data science, DevOps, security, and QA is essential to ensure seamless integration of ML and LLM models into production. Additionally, you will design and maintain efficient database schemas, optimize query performance, and implement security measures to protect sensitive data. Your responsibilities also include debugging, testing, and enhancing existing systems for robustness and efficiency, supporting production systems for high availability, fault tolerance, and performance optimization. Key Responsibilities: - Develop scalable and maintainable Python-based backend systems for ML workflows. - Build and optimize APIs and services using frameworks like Flask and Django. - Collaborate with cross-functional teams for seamless integration of ML and LLM models into production. - Design and maintain efficient database schemas, optimize query performance, and implement security measures. - Debug, test, and enhance existing systems for robustness and efficiency. - Support production systems for high availability, fault tolerance, and performance optimization. - Take end-to-end ownership of technical design and implementation from conceptualization to task execution. Qualifications Required: - 5+ years of professional Python development experience. - Expertise in at least one Python web framework such as Flask or Django. - Strong ORM and relational database management skills. - Cloud platform experience, preferably Azure (AWS acceptable). - Solid knowledge of SQL and query optimization techniques. - Bachelor's degree in Computer Science, Engineering, or equivalent. - Proficient problem-solving and collaboration capabilities. - Experience with Kubernetes (EKS), Docker, and managing CI/CD pipelines. - Skilled in building scalable data pipelines and integrating them within ML workflows. Preferred Skills (Nice-to-Have): - Familiarity with big data technologies such as Snowflake. - Experience with ML libraries including scikit-learn, pandas, PySpark, and PyArrow. - Database performance design and optimization experience. - Prior collaboration with data science teams is a plus.,