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Metro Bangalore and popular ML role increase applicants, counterbalanced by seniority and niche LLM/MLOps expectations.
Core ML/AI skills transfer across industries, but large-scale platform and LLM expertise require domain familiarity.
Explicit 9–14 years requirement plus many mandatory LLM, MLOps, and production skills narrows the candidate pool.
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Lead the design and development of advanced data science and machine learning models, including AI/ML solutions based on Large Language Models (LLMs) and Retrieval Augmented Generation (RAG).
Develop and optimize scalable machine learning pipelines for production environments, ensuring performance, scalability, reliability, and cost efficiency.
Collaborate with engineering and product teams to deploy, monitor, and continuously improve AI/ML solutions while establishing best practices for model evaluation, experimentation, governance, and responsible AI.
9 to 14 years of experience in data science, machine learning engineering, or applied AI roles.
Proficiency in Python and familiarity with at least one other programming language (Java or C++).
Hands-on experience with AI/ML frameworks/libraries (Scikit-learn, NumPy, Pandas, SciPy, Hugging Face Transformers) and Large Language Models including fine-tuning and deployment.
Experience with cloud platforms (AWS, Azure, Google Cloud), distributed computing frameworks (Spark, Ray), and production deployment of ML models.
Experienced leader in data science with deep expertise in advanced AI/ML techniques, especially LLMs, RAG, and AI agents.
Strong engineering background with proven ability to design and operationalize scalable, production-grade ML pipelines and systems.
Experienced in cross-functional collaboration to integrate AI/ML solutions into complex, cloud-scale product environments with governance and responsible AI practices.