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Tier-2 AI unicorn, hybrid Bangalore mid-level ML role increases applicants, but niche agentic/LLM skills moderate competition.
Core ML/LLM and systems expertise is transferable, though agentic AI product experience favors AI-focused employers.
Explicit 2–4 years, ML/LLM, cloud/container skills, and advanced degree preference enforce strict shortlisting.
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Lead research, design, development, and deployment of advanced AI agents and multi-agent systems with planning, decision-making, and execution capabilities.
Integrate large language models (LLMs) and AI techniques to enhance agent autonomy, building scalable infrastructure for AI agent operations at scale.
Collaborate cross-functionally to deliver agentic AI solutions and contribute technically to GenAI workflows enabling chat-like command execution.
2-4+ years of relevant machine learning work experience.
Master’s or Ph.D. in Computer Science, Artificial Intelligence, or related field, or equivalent experience.
Proficiency in Python and ML frameworks such as TensorFlow or PyTorch; experience with AWS, Docker, Kubernetes.
Knowledge of machine learning algorithms, Gen AI, LLMs, NLP, agent-based modeling, and autonomous systems.
Experienced in architecting and deploying complex, scalable AI multi-agent systems with LLM integration.
Strong coding and algorithmic skills with practical experience in distributed system design and microservices architecture.
Background in research or real-world implementation of agentic AI and reinforcement learning methods to build intelligent personalized AI agents.