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Tier-1 brand, mid-level AI generalist, broad GenAI/MLOps and cloud requirements increase candidate competition.
Specialized GenAI and cloud MLOps requirements moderately limit cross-industry transferability.
Explicit 4–6 years plus mandatory GenAI, MLOps, cloud and platform skills makes shortlisting highly strict.
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Design and implement end-to-end ML and GenAI solutions including RAG pipelines, LLM integrations, and prompt engineering frameworks.
Develop and deploy scalable API-based AI applications using FastAPI, Flask, or Plotly Dash, integrated with cloud-native platforms (Azure, GCP, AWS).
Build and optimize MLOps workflows including CI/CD, model registry, experiment tracking, automated retraining, and ETL/ELT data pipelines.
4-6 years of professional experience in AI/ML engineering with hands-on expertise in machine learning, GenAI, and cloud-native deployments.
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
Proficient in Python and SQL; working knowledge of HTML/CSS/JavaScript.
Experience with at least one cloud platform among Azure, GCP, or AWS including cloud services such as Azure App Service, Cloud Run, and data engineering tools like Azure Data Factory, BigQuery, or Databricks.
Experienced in building enterprise-grade ML pipelines and scalable AI solutions focusing on GenAI and LLM technologies.
Comfortable working across data engineering, MLOps, and deployment pipelines in multi-cloud environments.
Skilled in integrating multiple tools and frameworks for prompt engineering, model monitoring, and BI/reporting to produce AI-driven business outcomes.