Back
Moderate competition from a well-known employer and Bengaluru location, offset by seniority and niche LLM requirements.
Medium because ML/LLM skills transfer across industries, though enterprise model governance and finance preference increases specificity.
High due to explicit 8+ years requirement and mandatory ML/LLM, cloud, and MLOps skillset.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead end-to-end delivery of AI/ML and LLM-based solutions including design, development, deployment, and monitoring at enterprise scale.
Architect and implement advanced generative AI applications involving prompt engineering, fine-tuning, embeddings, vector search, and RAG.
Drive best practices in model evaluation, governance, responsible AI, and mentor data science teams while collaborating across business, engineering, and governance functions.
8+ years IT experience with 5+ years in ML/statistical modeling and AI development; 2+ years specifically in generative AI/LLMs.
Bachelor's or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or related quantitative field.
Proficiency in Python and ML frameworks like Scikit-learn, PyTorch, TensorFlow, Keras.
Hands-on experience with LLMs, NLP, embeddings, vector databases, RAG, and cloud platforms Azure/AWS for AI/ML deployment.
Deep technical expertise and proven track record architecting and operationalizing AI/ML models, especially generative AI and LLMs, at scale within enterprises.
Experience working cross-functionally with engineering, architecture, business stakeholders, and governance teams to build secure, scalable AI solutions.
Prior mentoring or leadership experience and familiarity with model governance, Responsible AI, regulatory compliance, and AI platform engineering concepts like MLOps/LLMOps.