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Tier-1 brand, generic AI title, metro location, and broad required skills increase competition.
Core ML and LLM skills are transferable, but specialized MLOps and generative AI needs increase domain sensitivity.
Preferred production AI, MLOps, and cloud skills but no explicit years yields medium strictness.
Design, develop, and implement AI and Generative AI solutions using LLMs, Agentic AI frameworks, RAG architectures, and cloud AI platforms.
Develop and maintain scalable AI/ML pipelines and data preparation workflows integrating with cloud-native platforms like Azure, Databricks, and AWS.
Manage deployment, testing, monitoring, and maintenance of AI/ML solutions following MLOps and LLMOps best practices with a focus on Responsible AI governance.
Bachelor’s degree in Computer Science, Data Science, AI/ML, or related technical field.
Work Experience Required: Hands-on experience in developing and successfully deploying production-level AI applications.
Proficiency in Python, Spark or SQL; familiarity with cloud AI platforms such as Azure, AWS, Databricks; and containerization technologies like Docker or Kubernetes.
Understanding of AI/ML workflows and basic knowledge of LLMs, Generative AI concepts, prompt engineering, and software development lifecycle.
Experienced in building cloud-native AI/ML pipelines and integrating AI across diverse data and application ecosystems.
Comfortable implementing AI governance including Responsible AI practices like bias detection and compliance with data ethics standards.
Adaptable to fast-evolving AI technologies and skilled in operationalizing AI solutions with a strong blend of business and technical acumen.