**Responsibilities**

Requirement Analysis: Partner with product and business teams to translate ambiguous business needs into well-defined ML problems through data-driven statistical analysis.

Exploratory Data Analysis (EDA): Perform in-depth exploratory analysis on large-scale datasets to understand data distributions, identify patterns and anomalies, and present findings using clear, insightful visualizations.

Data Insights & Feature Engineering: Analyze and extract key signals from massive datasets, engineer high-quality features, and collaborate with labeling teams to curate accurate ground-truth data.

Model Development & Optimization: Design, train, validate, and optimize machine learning and deep learning models (e.g., for image and text processing) to deliver high-performing, production-ready solutions.

Production Deployment: Build, deploy, and maintain scalable, reliable, and high-performance ML systems in production environments, ensuring ongoing monitoring, retraining, and performance tuning.

Continuous Improvement: Stay current with advancements in machine learning research, tools, and frameworks to continually enhance model accuracy, efficiency, and scalability.

**Skills Required:**

Educational Background: B.Tech or M.S. in Computer Science, Data Science, or a related field from a reputed institution, with a strong focus on practical delivery and applied problem-solving.

Machine Learning Expertise: Hands-on experience with leading ML frameworks such as PyTorch or TensorFlow, gained through academic projects, internships, or competitive platforms like Kaggle.

Software Engineering Excellence: Demonstrated ability to write clean, efficient, and production-quality code, adhering to high coding and testing standards.

Data Analysis & Engineering: Proficiency in data manipulation, analysis, and transformation using tools such as SQL, PySpark, or Pandas.

Cloud & MLOps Exposure: Familiarity with machine learning and data services on cloud platforms (AWS, Azure, or GCP), including experience with data pipelines and deployment workflows.

Core Computer Science Foundations: Solid understanding of computer architecture, operating systems, data structures, and algorithms.

Advanced ML Knowledge (Preferred): Understanding of Transformer architectures, their underlying mathematics, and internal mechanisms is a strong plus.

Analytical & Problem-Solving Skills: Strong ability to approach open-ended problems with curiosity, analytical rigor, and a data-driven mindset.

Machine Learning Engineer

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