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Mid-level AI role, popular title and metro location increase applicant density despite niche LLM skills.
Core ML/LLM skills are transferable across industries, though BFSI compliance experience adds advantage.
Multiple mandatory LLM, RAG, vector DB, Django, Docker, and explicit years requirement.
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Design, develop, and maintain production-grade Generative AI and LLM applications including RAG pipelines and AI services.
Build backend AI APIs and integrate LLMs with database systems like PostgreSQL and vector databases such as Qdrant.
Evaluate and optimize model performance, perform inference optimization, and manage deployment with tools like Docker and cloud environments.
3–5+ years of relevant software engineering or AI/ML experience.
Strong skills in Python programming, Generative AI, LLMs, RAG architectures, embeddings, vector databases (preferably Qdrant), SQL/PostgreSQL, and Django/REST API development.
Experience with LLM evaluation frameworks and deploying AI solutions in production environments.
Bachelor's or Master's degree in Computer Science, IT, AI/ML, Data Science, or related fields.
Proven ability to independently manage AI projects end-to-end from problem definition through data handling, prototyping, evaluation, API development, deployment, and monitoring.
Experience working on enterprise-scale AI applications, ideally with knowledge of banking, compliance, or sensitive data handling.
Strong technical depth in integrating and optimizing LLMs beyond basic API usage, including deployment and performance tuning.