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Tier-1 brand, mid-level ML role, and metro hiring create high applicant competition.
Deep GenAI, ML and MLOps requirements limit transferability from non-ML backgrounds.
Explicit 4–6 years plus mandatory GenAI, MLOps, cloud and data engineering skills make screening strict.
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Design and implement end-to-end ML and GenAI solutions including RAG pipelines, LLM integrations, prompt engineering, and evaluation frameworks.
Develop, deploy, and maintain API-based AI applications and scalable machine learning pipelines covering data ingestion through model deployment and monitoring.
Implement enterprise-grade MLOps workflows and cloud-native deployment on Azure, GCP, or AWS, including CI/CD, model registry, and automated retraining.
4–6 years of experience in AI/ML engineering with hands-on expertise in machine learning, GenAI, and cloud-native deployment.
Proficiency in Python (advanced), SQL, and working knowledge of web technologies (HTML/CSS/JavaScript).
Experience with cloud platforms Azure, GCP, or AWS including services like Azure App Service, Cloud Run, Azure Bot Service, Dialogflow.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
Experienced in building and deploying large language model-based applications and RAG architectures with prompt engineering expertise.
Proficient in end-to-end data engineering and ML pipeline development using tools like Azure Data Factory, BigQuery, Databricks, and vector databases.
Skilled at integrating MLOps practices in cloud environments using CI/CD, model monitoring, experiment tracking, and data governance frameworks.