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Tier-1 brand, mid-level AI role, metro location and broad skillset increase candidate competition.
Core ML and MLOps skills are broadly transferable across industries.
Requires production ML experience, cloud and MLOps skills, and Responsible AI practices, enforcing strict technical filters.
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Design, develop, and implement AI and Generative AI solutions including LLMs, Agentic AI frameworks, RAG architectures, and cloud AI platforms.
Develop and maintain scalable AI/ML pipelines, data workflows, and cloud-native integrations (Azure, Databricks, AWS).
Support deployment, testing, monitoring, and maintenance of AI/ML solutions with MLOps and LLMOps best practices and implement AI governance and Responsible AI practices.
Bachelor’s degree in Computer Science, Data Science, AI/ML, or related technical field.
Hands-on experience in developing and deploying production-level AI applications.
Experience with Python, Spark or SQL, and exposure to cloud platforms like Azure, AWS, or Databricks.
Work Experience Required: Not explicitly mentioned in the JD.
Able to operate at the intersection of AI engineering and cloud-native environments with practical MLOps and LLMOps knowledge.
Experienced in integrating AI with APIs, databases, container orchestration (Docker/Kubernetes), and cloud services in production environments.
Familiar with AI governance including bias detection, hallucination mitigation, and compliance for responsible AI deployment.