Senior Data Science and Machine Learning Engineer

Senior Data Science and Machine Learning Engineer Duration: 6 Months (C2H) EST - Remote 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.

Education Requirement Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field. Master's degree or PhD is a strong plus. 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.

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