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Tier-1 brand, metro location, and popular mid-level ML role increase candidate competition.
Core ML/LLM engineering skills transfer across industries, though sustainability assurance focus adds moderate specificity.
Multiple mandatory ML/LLM and deployment skills imply technically strict screening despite no explicit years.
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Develop, deploy, and maintain end-to-end machine learning and AI models including LLM-based applications such as RAG pipelines and chatbots.
Build and integrate REST APIs and FastAPI services to support AI model deployment and enterprise system integration.
Collaborate with business and Agile Scrum teams to gather requirements, deliver scalable AI solutions, and monitor model performance ensuring data quality and reliability.
Bachelor's or Master's degree in Computer Science, Data Science, AI, Statistics, Mathematics, or related field.
Strong experience with Python, SQL, machine learning techniques, REST API and FastAPI development.
Proficient in statistical modeling methods (Regression, Logistic Regression, Clustering, Decision Trees, Random Forests, KNN, Ensemble Methods).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building and deploying production-level machine learning and LLM-based solutions with integration via APIs.
Familiarity with AI agent frameworks like LangChain or Semantic Kernel and concepts like prompt engineering and RAG architectures.
Comfortable working in Agile environments collaborating with stakeholders and capable of implementing monitoring and observability for AI applications.