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Tier-1 brand, metro location, and mid-level AI role create high applicant competition.
Generative AI engineering skills are transferable across industries but require specialized ML/LLM expertise.
Explicit 2–4 year requirement plus mandatory ML, LLM, and framework experience enforces strict filtering.
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Design, develop, and deploy AI/ML and Generative AI solutions including AI agents, chatbots, and intelligent automation workflows.
Build, fine-tune, and optimize machine learning models focusing on Large Language Models and Retrieval-Augmented Generation (RAG) architectures with attention to accuracy, scalability, and performance.
Collaborate across product, engineering, and business teams to deliver AI-driven solutions applying responsible AI practices including security, privacy, and ethical considerations.
2–4 years of experience in AI/ML, Data Science, or Software Engineering.
Strong programming skills in Python with hands-on experience in Machine Learning and Deep Learning model development.
Experience with Generative AI, LLMs, prompt engineering, and frameworks such as LangChain, LlamaIndex, Hugging Face Transformers, PyTorch, TensorFlow, OpenAI, or Azure OpenAI APIs.
Knowledge of RAG architectures, vector databases, embeddings, REST APIs, microservices, Git, CI/CD, Agile methodologies, and a degree in Computer Science, Artificial Intelligence, Data Science, or related field.
Experienced in implementing Large Language Models and Generative AI solutions in a collaborative, cross-functional environment.
Skilled in applying responsible AI principles including security, privacy, and ethics within AI/ML projects.
Comfortable working in hybrid mode (2 days/week onsite) with exposure to cloud platforms, MLOps, and possibly AI agents, NLP, Computer Vision, or Conversational AI technologies.