Artificial Intelligence Engineer

Roles and Responsibilities

• Develop AI Solutions: Design, build, and deploy end-to-end Machine Learning, Deep Learning, and Generative AI models.
• Implement GenAI & LLMs: Develop advanced Generative AI applications using Large Language Models (LLMs), RAG pipelines, and fine-tuning techniques.
• Build AI Pipelines: Create scalable data pipelines, feature stores, and integrate AI models with core product APIs.
• Manage MLOps: Deploy models in production using Docker and Kubernetes, ensuring proper tracking, versioning, and monitoring.
• Optimize Performance: Evaluate and benchmark model latency, accuracy, and cost to ensure enterprise-grade efficiency.
• Collaborate Cross-Functionally: Work closely with Data Engineers, Product Managers, and Backend Teams to align AI capabilities with business goals.

Preferred Candidate Profile

• Education: B.E. / B.Tech / M.Tech / MS in Computer Science, Data Science, AI, or a related quantitative field.
• Experience: Minimum [X] years of professional experience as an AI/ML Engineer, with at least [Y] years focused on Generative AI and LLMs.
• Core Coding: Strong, production-grade programming skills in Python, SQL, and RESTful API development.
• Framework Mastery: Hands-on experience with PyTorch/TensorFlow, Hugging Face, LangChain/LlamaIndex, and Vector Databases.
• Cloud & DevOps: Proven expertise in AWS, Azure, or GCP, alongside MLOps tools (Docker, CI/CD, MLflow).
• Problem Solving: Excellent analytical skills with a track record of moving AI models from research to production.