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Mid-level position, metro location, notable employer, and broad skillset requirements increase competition.
Core ML, NLP, MLOps, and cloud skills are easily transferable across industries.
Explicit 4–7 years plus mandatory ML, MLOps, GCP, OpenShift, Python, and deployment skills enforce strict shortlisting.
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Design, develop, train, fine-tune, and deploy AI/ML models specializing in Natural Language Processing (NLP) and Generative AI technologies.
Manage MLOps lifecycle including model deployment, monitoring, and optimization using Python, Project Jupyter, and cloud platforms (Red Hat OpenShift, GCP).
Develop self-service analytics solutions and AI agents with agentic workflows, collaborating with cross-functional teams throughout product lifecycle.
4 to 7 years of experience in AI/ML engineering with focus on NLP and Generative AI.
Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.
Strong hands-on skills in Python, SQL, Docker, microservices, Pandas, NumPy, Flask, and MLOps practices.
Experience working with hybrid cloud environments, specifically Red Hat OpenShift and Google Cloud Platform (GCP).
Experienced in building scalable, production-ready AI solutions in hybrid and cloud-native environments with rigorous MLOps discipline.
Comfortable working in cross-functional teams including product managers, data engineers, and backend developers to deliver AI-powered business solutions.
Has expertise specifically in intelligent AI agents, agentic workflows, and state-of-the-art NLP and Generative AI model technologies.