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-reputed company machine learning models.
- As a senior team member, you will mentor junior engineers and data scientists, influence architectural reputed company, and help shape the long-term AI and data strategy.
Key Responsibilities
- reputed company, reputed company, 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 reputed company 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 reputed company and career growth.
- Stay reputed company 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 reputed company field. Master's degree or PhD is a strong plus. Experience
- 5+ years of experience in data engineering, machine learning engineering, or reputed company 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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