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Specialized LLM/MLOps senior role in a metro hub reduces candidate density despite company visibility.
ML/LLM engineering skills transfer across industries, but MLOps and vector DB expertise raise domain specificity.
Explicit 7–12 years plus advanced degree and specific LLM/MLOps technologies imply high shortlisting strictness.
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Design, develop, fine-tune, and deploy Large Language Models (LLMs) and Generative AI applications using Python and ML frameworks.
Build and optimize Retrieval-Augmented Generation (RAG) solutions, prompt engineering frameworks, and AI agents with scalable data pipelines using SQL.
Implement MLOps best practices including model deployment, monitoring, CI/CD, and automation, collaborating with cross-functional teams to deliver AI-powered solutions.
7 to 12 years of experience in AI/ML engineering, data science, or related domains.
Master's or PhD 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 CI/CD and model monitoring.
Preferred cloud experience with Azure, AWS, or GCP.
Experienced in working with LLMs, Generative AI, NLP, vector databases, embeddings, and prompt engineering techniques.
Proficient in developing end-to-end machine learning solutions with focus on scalability, security, and cost efficiency.
Capable of collaborating across product, engineering, and data teams in a fast-paced, cutting-edge technology environment.