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Tier-1 brand and mid-level AI management make the applicant pool moderately competitive.
Specialized ML/GenAI, MLOps, and enterprise AI governance skills are transferable yet domain-focused.
Explicit 5–8 years plus mandatory ML, GCP, LLMOps, and leadership requirements increase filtering rigor.
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Own end-to-end AI project lifecycles from data acquisition through production deployment, monitoring, and optimization, ensuring measurable business outcomes.
Lead and mentor AI engineers and data scientists while establishing enterprise-grade MLOps and LLMOps best practices.
Collaborate with business and technical stakeholders to drive AI strategy, governance (Responsible AI), and delivery of scalable AI/ML and Generative AI solutions using cloud platforms (GCP).
Bachelor's degree in Data Science, Machine Learning, Computer Science, Statistics, Applied Mathematics, IT, or equivalent.
5 to 8 years experience applying AI/ML solutions in enterprise environments and using Python-based AI/ML technologies.
Experience leading AI or Data Science teams and acting as a senior technical lead with cloud AI platform exposure (GCP preferred).
Hands-on experience with Generative AI technologies and enterprise AI deployment.
Experienced in driving AI/ML strategy including predictive analytics, Generative AI, and automation in large enterprise settings.
Proficient in architecting and operating end-to-end AI/ML systems leveraging TensorFlow, PyTorch, and GCP AI services with scalable MLOps/LLMOps pipelines.
Capable of leading cross-functional programs, managing AI governance frameworks, and influencing executive stakeholders with measurable impact insights.