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Popular AI title, mid-level (2-5 yrs), metro location and broad LLM/MLOps skillset drive high competition.
Requires specialized ML/LLM and MLOps expertise, moderately limiting cross-industry transferability.
Explicit 2–5 year requirement plus mandatory LLM, MLOps, cloud, and deployment skills imply high shortlisting strictness.
Design, build, and operationalise machine learning and generative AI systems from prototype to production.
Develop and fine-tune LLM-based and agentic applications including RAG pipelines and tool use with focus on accuracy, latency, and cost optimization.
Automate data pipelines and package models as scalable APIs using containerization, CI/CD, and MLOps for deployment and monitoring.
2-5 years of work experience in AI/ML or related roles.
Degree in Computer Science, IT, Engineering, Mathematics, Statistics, or related quantitative field (B.E./B.Tech, M.Tech, B.Sc., M.Sc., MCA).
Proficient in Python programming and experience with ML/DL frameworks (scikit-learn, PyTorch, TensorFlow).
Experience with LLM APIs, prompt engineering, agent frameworks (LangChain, LangGraph, MCP), RAG, vector databases, SQL, cloud platforms (AWS, Azure, or GCP), and MLOps tools.
Experienced in deploying and optimizing ML models in production environments with strong operational ownership.
Skilled in working with LLMs, agentic applications, and cutting-edge AI tools and frameworks relevant to generative AI.
Comfortable working in fast-evolving tech landscapes requiring structured problem-solving and critical evaluation of AI outputs.