AI/ML Engineer

Key Responsibilities: 

• Develop ML and Deep Learning Solutions: Create predictive, classification, and optimization models using supervised, unsupervised, and reinforcement learning.
• Design and Implement Generative AI Systems: Build and fine-tune Large Language Models (LLMs) and diffusion models for a range of use cases involving text, code, and multi-modal data.
• Build Scalable Recommendation Engines: Develop and optimize recommendation systems using collaborative filtering, content-based filtering, hybrid models, or sequential models.
• Cloud-Native ML Engineering: Deploy and manage machine learning pipelines and APIs in cloud environments (AWS, GCP, Azure),ensuring scalability, observability, and cost efficiency.
• End-to-End ML Lifecycle Ownership: Own the model lifecycle from feature engineering and experimentation to deployment, CI/CD, monitoring, and iteration.
• Collaborate Across Functions: Work with cross-functional teams including product managers, engineers, and data scientists to translate business goals into AI-powered solutions.