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Tier-1 employer, mid-level ML/GenAI role, metro appeal creates high competition.
Specialized LLM, GenAI, MLOps, and cloud expertise limits transferability across non-AI roles.
Explicit 4–8 year requirement plus mandatory GenAI, cloud, and MLOps skills enforces strict filters.
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Design, develop, and deploy end-to-end AI/ML and GenAI solutions including LLM integrations, RAG pipelines, and prompt engineering.
Build and manage scalable ML pipelines spanning data ingestion, feature engineering, model training, deployment, MLOps CI/CD, and monitoring in cloud environments (Azure, GCP, AWS).
Develop API-based AI applications and implement cloud-native deployment using services like Azure App Service, Cloud Run, and Azure Bot Service.
4 to 8 years of experience in AI/ML engineering with hands-on expertise in GenAI and cloud-native AI application deployment.
Advanced Python programming skills and strong SQL knowledge, with working knowledge of HTML, CSS, and JavaScript.
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
Experience with cloud platforms (Azure/GCP/AWS), MLOps (CI/CD, model registry, monitoring), and data engineering tools like Azure Data Factory, BigQuery, and Databricks.
Proven ability to deliver scalable AI solutions leveraging LLMs, GenAI frameworks, and RAG pipeline architectures across multi-cloud platforms.
Experienced in building enterprise-grade MLOps workflows ensuring model lifecycle management including experiment tracking and automated retraining.
Comfortable working with cross-functional teams to translate business needs into technical AI/ML solutions, with strong skills in API development and cloud service deployment.