Data Science and Machine Learning Engineer

Job Summary

• We are seeking a Senior Data Science Engineer to design, build, and scale data-driven systems that power advanced analytics and machine learning across our organization. This role sits at the intersection of software engineering and data science; you'll be responsible for building robust data pipelines, enabling experimentation, and deploying production-ready machine learning models.
• As a senior team member, you will mentor junior engineers and data scientists, influence architectural decisions, and help shape the long-term AI and data strategy.‍

Key Responsibilities

· Develop, deploy, and maintain machine learning models in production environments.

· Collaborate with data scientists, analysts, and product managers to define and deliver data-driven features.

· Ensure high-quality data through monitoring, validation, and robust testing frameworks.

· Architect and maintain data platforms and tools for experimentation, model serving, and feature engineering.

· Explore and integrate Large Language Models (LLMs) and other generative AI approaches into business applications and data workflows.

· Contribute to code reviews, technical design discussions, and best practices for the team.

· Mentor and guide junior engineers/data scientists, fostering technical excellence and career growth.

· Stay current with emerging technologies in Data Science, Machine Learning, LLM Ops, ML Ops.‍

Experience

• 5+ years of experience in data engineering, machine learning engineering, or related roles.
• Strong proficiency in Python (Pandas, NumPy, PySpark, or similar).
• Solid understanding of ML model development, training, and deployment pipelines.
• Experience with ML model monitoring and observability frameworks.
• Experience with deep learning frameworks(TensorFlow, PyTorch).
• Familiarity with CI/CD, version control (Git),and modern ML Ops practices.
• Contributions to open-source Data Science / Machine Learning libraries or frameworks.
• Hands-on experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
• Proficiency with SQL and database systems (PostgreSQL, MySQL, or NoSQL alternatives).
• Exposure to data governance, security, and compliance requirements.
• Knowledge of experiment design (A/B testing, causal inference).