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Mid-level role in metro with common experience band but niche GenAI skillset reduces applicant density.
Core ML and LLM skills transfer across industries, but agentic and vector DB expertise adds domain specificity.
Explicit 3–7 years plus mandatory LLM, RAG, vector DB, cloud, Docker and Kubernetes increases filtering.
Design, build, and deploy scalable AI applications leveraging Agentic AI frameworks and GenAI technologies.
Transform AI architecture into production-grade solutions including RAG pipelines, agent-based applications, and knowledge retrieval systems.
Manage cloud-based deployments and optimize AI workflows for performance and reliability across AWS, Azure, or GCP.
3–7 years of relevant experience in AI/ML or full-stack AI solution development.
Proficient in Python with experience in LLMs, Transformer models, Agentic AI frameworks, prompt engineering, and API integration.
Knowledge of RAG architectures, vector databases, SQL/NoSQL, and model lifecycle tools like CI/CD, Docker, Kubernetes.
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
Experienced in developing and deploying Agentic AI solutions focused on scalable, production-grade implementations.
Skilled in end-to-end AI workflows including data preprocessing, feature engineering, and model optimization.
Familiar with cloud infrastructure management and best coding practices in collaborative environments.