Engineer - Machine Learning

About this role

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Skills you'll use

What you'll do

• AI/ML Solution Development
• Develop, test, deploy, and maintain Machine Learning and AI-based applications for LMS, Student Portal, and other academic systems.
• Build intelligent automation solutions for student engagement, learning analytics, recommendation engines, chatbot support, assessment analytics, predictive analytics, and workflow optimization.
• Support integration of AI/ML models with existing web applications and enterprise systems.
• Product & Technology Collaboration
• Work closely with the Product Manager to understand business requirements, change requests, enhancement needs, and digital transformation initiatives.
• Coordinate with outsourced Tech Team for solution design, development timelines, testing, deployment, and issue resolution.
• Participate in requirement gathering discussions with cross-functional teams including Academics, Operations, Student Support, Admissions, and Examination departments.
• Data & Analytics
• Build and maintain data pipelines, dashboards, and analytical models for academic and operational insights.

Who can apply

• Strong understanding of Machine Learning concepts, algorithms, model development lifecycle, and AI/ML application deployment.
• Hands-on experience in Python and ML frameworks/libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, etc.
• Knowledge of NLP, Generative AI, Recommendation Systems, Predictive Analytics, Deep Learning, APIs, database systems, and integration architecture.
• Familiarity with cloud platforms, version control tools, deployment environments, SQL, data visualization, and analytics tools.
• Strong analytical, problem-solving, and critical thinking abilities with capability to convert business requirements into technology solutions.
• Good stakeholder management, coordination, documentation, and communication skills.
• Ability to work collaboratively with cross-functional teams including Product, Technology, Academics, Operations, and external vendors.
• High ownership mindset with focus on reliability, continuity, process improvement, and institutional knowledge retention.
• Attention to detail and quality orientation.
• Ability to manage multiple priorities, timelines, and deliverables in a dynamic environment.

About NMIMS

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