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Tier‑1 brand and metro location increase competition, but niche LLM/MLOps requirements limit applicant pool.
Advanced LLM and MLOps skills are transferable across industries, though enterprise banking integrations add specificity.
Explicit 7–11 years and many mandatory LLM, MLOps, and vector DB skills create high shortlisting strictness.
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Contribute to development and implementation of Python application systems with focus on generative AI capabilities.
Build, tune, and integrate large language model (LLM) based applications using tools like OpenAI, Anthropic Claude, Google Gemini, and open-source LLMs.
Develop and deploy generative AI solutions utilizing APIs, knowledge graphs, vector databases, and orchestration platforms in enterprise environments.
7-11 years of experience in Python application development with strong exposure to generative AI.
Proficiency in Python and related libraries such as Transformers, Pandas, scikit-learn, Lang Chain, LlamaIndex, PyTorch, or TensorFlow.
Experience working with LLM platforms (OpenAI, Anthropic Claude, Google Gemini, open-source LLMs) and knowledge of RAG pipelines and vector databases.
Bachelor’s degree or equivalent experience.
Experienced in end-to-end generative AI application development with hands-on programming and deployment skills.
Familiar with ML Ops practices including model evaluation, prompt engineering, and deployment pipelines.
Able to translate AI capabilities into enterprise-integrated solutions using APIs, knowledge graphs, and orchestration tools.