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Mid-level ML/LLM role in metro, popular skillset, and Series-C startup increases applicant competition.
Highly specialized ML/NLP and LLM experience limits cross-industry portability.
Mandatory 3+ years plus tier-1 institute degree and specific ML/NLP, deployment, and vector DB skills make filters strict.
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Build and deploy Large Language Model (LLM)-powered agents for internal and customer-facing applications.
Design and improve retrieval-augmented generation (RAG) pipelines, prompt engineering workflows, and integration of related AI tools.
Optimize ML and LLM systems for accuracy, latency, scalability, and cost while collaborating with cross-functional teams to deliver impactful AI features.
3+ years of industry experience in Machine Learning, NLP, or applied AI.
Bachelor's, Master's or PhD in Computer Science or mathematics-related field from tier-1 engineering institutes.
Strong programming skills in Python and experience with PyTorch and Hugging Face Transformers.
Experience with ML service deployment using REST APIs, Docker, Kubernetes, or cloud platforms.
Experienced with RAG and agentic AI concepts relevant to LLM-powered AI agents.
Skilled in model training, inference optimization, and GPU utilization for scalable AI applications.
Familiarity with production-ready AI/NLP pipelines including monitoring, evaluation benchmarks and continuous improvement loops.