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Popular ML/LLM role, metro location, and broad cloud/LLM requirements increase applicant density.
ML and MLOps skills are transferable across industries, though LLM specialization moderately narrows fit.
Explicit 3–11 years plus many mandatory ML, LLM, cloud, and MLOps requirements increases filter rigidity.
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Design, develop, train, and deploy machine learning and generative AI models for enterprise-scale applications.
Build, optimize, and monitor end-to-end ML pipelines and production deployments on cloud AI platforms ensuring scalability and reliability.
Implement MLOps best practices including model versioning, monitoring, and CI/CD automation to maintain operational ML workflows.
3–11 years of professional experience in AI, Machine Learning, or Data Science.
Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or related field.
Hands-on experience with cloud AI platforms such as GCP Vertex AI, Azure Machine Learning, or AWS SageMaker.
Proficiency in Python and machine learning frameworks like TensorFlow or PyTorch.
Experienced in building generative AI and Large Language Model (LLM) powered applications using frameworks like Hugging Face and LangChain.
Skilled in deploying and managing ML models using MLOps tools including Docker, Kubernetes, and CI/CD pipelines.
Familiar with cloud-native AI architectures, data engineering tools like Databricks, BigQuery ML, and concepts such as Retrieval-Augmented Generation (RAG).