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Mid-level, popular ML role with broad GenAI/cloud/MLOps requirements increases candidate competition.
Core ML and MLOps skills transferable, but GenAI and cloud-platform specifics raise moderate domain specificity.
Explicit 4–6 years plus extensive GenAI, cloud, and MLOps toolset enforces strict filtering.
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Design and implement end-to-end ML and GenAI solutions including RAG pipelines, LLM integrations, and prompt engineering.
Develop and deploy scalable API-based AI applications and MLOps pipelines on cloud-native platforms (Azure, GCP, AWS).
Build and optimize data engineering pipelines and BI dashboards while ensuring secure, cost-effective cloud deployment.
4–6 years of hands-on experience in AI/ML engineering with end-to-end machine learning and GenAI solution development.
Strong programming skills in Python and SQL; working knowledge of HTML/CSS/JavaScript.
Experience deploying ML workloads and MLOps on at least one cloud platform: Azure, GCP, or AWS.
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
Expertise in Large Language Models (LLMs), RAG architectures, prompt engineering, and vector search techniques.
Experienced in implementing cloud-native ML pipelines using Azure Data Factory, BigQuery, Databricks, and MLOps tools such as CI/CD, model registry, and experiment tracking.
Skilled in building enterprise-grade AI applications integrating data engineering, BI tools, and secure scalable deployment on multi-cloud environments.