Senior AI/ML Engineer (ID:3402)

• Design, develop, and optimize data pipelines and workflows supporting advanced analytics and AI/ML solutions.
• Implement and maintain scalable machine learning and AI engineering frameworks for enterprise-grade applications.
• Build, train, deploy, and optimize ML models at scale, ensuring performance, accuracy, and reliability.
• Develop AI-driven solutions for predictive analytics, automation, and intelligent decision-making.
• Apply GenAI tools and frameworks to deliver modern AI use cases.
• Implement AI algorithms for NLP, computer vision, and recommendation systems.
• Ensure adherence to data governance, security, compliance, and performance best practices.
• Support analytics platform and AI infrastructure deployments where required.
• Contribute to Center of Excellence (CoE) initiatives, standards, and reusable frameworks., * Opportunities to work on enterprise-scale AI, ML, and GenAI initiatives within a Center of Excellence.
• Exposure to advanced analytics platforms, AI frameworks, and data engineering ecosystems.
• Challenging projects involving predictive analytics, automation, and intelligent systems.
• Engagement with cloud-based AI/ML platforms and modern engineering practices.

• 8-10 years of overall experience in the Data Analytics, AI, and Machine Learning domain.
• Proven experience in Machine Learning Engineering, including: Building, training, and deploying ML models at scale and hands-on experience with TensorFlow, PyTorch, Scikit-learn.
• Solid experience in AI Engineering, including: Developing AI-driven solutions for analytics and automation and hands-on exposure to GenAI tools and frameworks.
• Implementing AI algorithms for NLP, computer vision, or recommender systems.
• Platform knowledge at an intermediate level, with strong conceptual depth in AI/ML systems.
• External certifications in Databricks and/or Microsoft Azure.

You should possess the ability to:

• Design scalable, secure, and high-performing AI/ML solutions for enterprise use cases.
• Apply analytical thinking and structured problem-solving to complex data and AI challenges.
• Adapt quickly to fast-paced environments and evolving AI technologies.
• Lead initiatives within a Center of Excellence, including best practices, standards, and mentoring.