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Tier-1 brand, popular ML role, mid-level experience, and broad full-stack GenAI requirements increase competition.
Specialized GenAI, cloud, and MLOps expertise reduce cross-industry transferability, so background sensitivity is high.
Explicit 4–6 years plus mandated LLM, cloud, MLOps, and data engineering skills make shortlisting highly strict.
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Design and implement end-to-end ML and GenAI solutions including LLM integrations, RAG pipelines, and prompt engineering.
Develop and deploy API-based AI applications and scalable ML pipelines encompassing data ingestion to model monitoring.
Build and maintain MLOps workflows and cloud-native deployments using Azure, GCP, or AWS with focus on automation, security, and cost optimization.
4–6 years of professional experience in AI/ML engineering or related fields.
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related discipline.
Strong programming skills in Python and SQL, with working knowledge of HTML/CSS/JavaScript.
Experience in cloud-native deployment on Azure, GCP, or AWS environments, including MLOps pipeline construction.
Experienced in designing and deploying LLM-based and Generative AI solutions using frameworks like LangChain and cloud AI services.
Proficient in building end-to-end data pipelines and implementing MLOps for enterprise-grade operationalization of AI models.
Comfortable working across multiple cloud platforms (Azure, GCP, AWS) with strong skills in data engineering, DevOps, and BI tools integration.