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Brand, mid-level ML role, metro context, and broad skillset increase applicant density.
Requires specialized GenAI, LLM, and cloud MLOps skills, moderately limiting cross-industry transferability.
Explicit 4–6 years plus many mandatory ML, cloud, and MLOps technologies increases filtering strictness.
Design and implement scalable ML and GenAI solutions including RAG pipelines, LLM integrations, and prompt engineering frameworks.
Build and deploy API-based AI applications using frameworks like FastAPI or Flask and manage end-to-end ML pipelines from data ingestion to model monitoring.
Deploy and optimize AI workloads on Azure, GCP, or AWS cloud-native platforms with full MLOps workflows for CI/CD, model registry, and automated retraining.
4 to 6 years of experience in AI/ML engineering or related roles.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
Strong hands-on expertise in Python programming and SQL, plus working knowledge of HTML/CSS/JavaScript.
Experience with cloud platforms and tools such as Azure, GCP, AWS, and related AI/ML services for deployment and MLOps.
Deep expertise in designing and deploying large language model (LLM) based applications and generative AI solutions with prompt engineering.
Proven ability to build and manage enterprise-grade MLOps pipelines integrating cloud-native tools on Azure, GCP, or AWS.
Skilled in end-to-end data engineering including ETL/ELT pipelines, data governance, and BI dashboard creation to deliver AI-driven business outcomes.