Machine Learning Engineer

Roles and Responsibilities

• Design, develop, and implement machine learning models for data analysis, predictive analytics, and AI-driven solutions.
• Utilize supervised and unsupervised learning techniques, including Generative AI (GenAI) and Large Language Models (LLMs), to build and optimize advanced ML systems.
• Perform data analysis, preprocessing, and feature engineering to support model training and integration.
• Collaborate with cross-functional teams to integrate ML solutions with source systems and ensure smooth data flow.
• Continuously improve models through iterative testing, tuning, and performance optimization.
• Contribute to the development of ML workflows, pipelines, and model deployment strategies.
• Stay updated on industry trends and emerging technologies in machine learning and AI.

Must-Haves

• Proficiency in Python programming, with strong knowledge of relevant libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, etc.
• Strong understanding of Linux operating systems and related tools for efficient development and deployment.
• Extensive knowledge and hands-on experience with supervised and unsupervised learning, as well as Generative AI (GenAI) and Large Language Models (LLMs).
• Experience in data analysis and building advanced machine learning models.
• Expertise in integrating ML solutions with source systems to ensure seamless data handling.
• Familiarity with version control systems (e.g., Git) and collaboration tools (e.g., Jira, Confluence).

Nice to Have

• Experience with cloud-based ML platforms (AWS, GCP, Azure).
• Knowledge of MLOps principles for model deployment and lifecycle management.

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