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Metro location and broad ML/GenAI skillset create moderate applicant competition.
Role requires specialized ML/LLM expertise, limiting cross-domain transferability.
Explicit 7–12 years, Master's/PhD and specialized LLM/MLOps requirements make filters strict.
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Design, develop, fine-tune, and deploy Large Language Models (LLMs), Generative AI, NLP, and AI applications with a focus on model performance, scalability, and security.
Implement MLOps best practices including model deployment, continuous integration/continuous delivery (CI/CD), monitoring, and automation for AI/ML models.
Collaborate with cross-functional teams to build and optimize AI-powered solutions involving vector databases, RAG (Retrieval-Augmented Generation), prompt engineering, and AI agent frameworks.
7 to 12 years of experience in AI/ML engineering, data science, or related domains.
Master's or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related field from a reputed university.
Strong hands-on experience with Python, SQL, ML frameworks (TensorFlow, PyTorch, Scikit-learn), and MLOps including model deployment and CI/CD.
Experience with Large Language Models, Generative AI, NLP, vector databases, prompt engineering, and cloud platforms (Azure, AWS, or GCP) preferred but must-have is AI/ML expertise.
Experienced in building scalable AI/ML solutions integrating LLMs and state-of-the-art NLP techniques in production environments with MLOps best practices.
Highly skilled in Python programming and ML frameworks with a strong understanding of model fine-tuning, prompt engineering, and deployment automation.
Capable of working cross-functionally with product, engineering, and data teams to deliver complex AI-driven applications efficiently, ensuring performance, cost, and security optimization.