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Tier-1 brand, mid-level demand, and metro Bangalore location attract many qualified applicants.
GenAI and LLMOps skills transfer across industries, though enterprise integration experience raises specificity.
Explicit 5+ years and 1–2 years GenAI plus specific LLM, vector DB, and MLOps requirements make screening strict.
Design, develop, and deploy large language model (LLM) powered applications including chatbots, copilots, and document intelligence systems.
Build and optimize Retrieval-Augmented Generation (RAG) pipelines and autonomous AI workflows integrating with enterprise systems and APIs.
Implement LLMOps practices such as monitoring, evaluation, versioning, and cost optimization while ensuring security and responsible AI governance.
Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field.
5+ years of software or machine learning engineering experience, with at least 1–2 years specifically in Generative AI or LLM-based systems.
Strong Python programming skills and hands-on experience with GenAI frameworks (OpenAI, Hugging Face, Anthropic, Google, etc.), prompt engineering, embeddings/vector databases, and related architectures.
Experience with cloud platforms (AWS, Azure, GCP), API development, microservices, and knowledge of MLOps/LLMOps practices.
Experienced in production-grade GenAI systems emphasizing LLM integrations, prompt workflows, and enterprise data pipelines.
Demonstrates hands-on expertise with agentic AI frameworks, multimodal AI (text and image), fine-tuning foundation models, and building AI copilots or automation systems.
Able to collaborate cross-functionally within product, data, and platform teams to deliver scalable, secure AI business solutions in an enterprise environment.