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Tier-1 brand and Bangalore metro boost competition, but senior specialization in LLMs moderates applicant density.
Highly specialized LLM/agentic AI and telecom AIOps emphasis requires domain-specific experience, reducing transferability.
Explicit 8–14 years plus mandatory LLM, fine-tuning, RLHF, and production deployment skills create strict filters.
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Lead design and implementation of machine learning and Agentic AI systems including multi-agent pipelines, autonomous reasoning, and tool-using agents for network operations.
Design and deploy LLM-powered applications encompassing RAG pipelines, prompt engineering, fine-tuning, and evaluation frameworks.
Own end-to-end AI/ML projects collaborating with cross-functional teams to deliver production-grade, scalable solutions with focus on interpretability, robustness, and performance monitoring.
8–14 years of applied Data Science and Machine Learning experience with progressive ownership of complex systems.
Hands-on experience with classical ML methods and building LLM-based systems (RAG, prompt engineering, vector DBs, orchestration).
Proficiency in Python programming and SQL; strong software engineering practices with CI/CD awareness.
Master's or PhD in Computer Science, Statistics, Mathematics, or related field (academic credentials subject to verification).
Experienced in designing and deploying Agentic AI workflows and LLM orchestration frameworks in production environments.
Demonstrates strong end-to-end ownership from problem framing through production scaling and iteration of AI/ML projects.
Domain knowledge or interest in telecom, network operations, or AIOps is highly desirable, with ability to collaborate across engineering and product teams.