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Tier-1 brand, mid-level ML role with broad GenAI/cloud/MLOps requirements increases competition.
Highly specialized GenAI, MLOps, and cloud skills limit cross-industry transferability.
Explicit 4–6 years plus mandatory GenAI, cloud, and MLOps stack makes filters highly stringent.
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Design and implement end-to-end AI/ML and GenAI solutions including RAG pipelines, LLM integrations, and prompt engineering.
Develop, deploy, and monitor scalable API-based AI applications on cloud-native platforms (Azure, GCP, AWS).
Build and optimize MLOps workflows involving CI/CD, model registry, experiment tracking, and automated retraining.
4–6 years of professional experience in AI/ML engineering.
Proficiency in Python (advanced), SQL, and working knowledge of HTML/CSS/JavaScript.
Experience with cloud environments (Azure, GCP, AWS) and associated ML and data engineering tools (Azure Data Factory, BigQuery, Databricks).
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
Strong expertise in large language models (LLMs), prompt engineering, and GenAI frameworks (LangChain, Azure OpenAI, AWS Bedrock).
Experience in building enterprise-grade MLOps pipelines and deploying AI applications in multi-cloud environments.
Hands-on skills across data engineering, BI tools, and scalable cloud-native AI system deployment.