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Tier-1 brand, mid-level experience band, and metro hiring increase applicant competition.
AI engineering skills are transferable, but managerial and GenAI specialization moderately reduce cross-industry fit.
Explicit 5–8 years, required AI/MLOps/GCP stack and leadership experience make filters strict.
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Lead design, development, deployment, and scaling of AI/ML and Generative AI solutions across enterprise domain.
Own AI project lifecycles from data acquisition through production deployment, monitoring, and optimisation using MLOps/LLMOps practices.
Manage and mentor AI engineers and data scientists while collaborating with stakeholders to align AI initiatives with measurable business outcomes.
Bachelor’s degree in Data Science, Machine Learning, Computer Science, Statistics, Applied Mathematics, IT, or equivalent.
5 to 8 years of experience applying AI/ML solutions and Python-based AI/ML technologies in enterprise environments.
Experience leading AI or Data Science teams and acting as senior technical lead on solution and architectural decisions.
Hands-on experience with Generative AI technologies and Cloud AI Platforms (GCP preferred).
Experienced in strategic AI leadership combining technical expertise with business alignment and AI governance.
Strong technical leader with proficiency in TensorFlow, PyTorch, Python, GCP AI/ML services, and enterprise-level AI system architecture.
Demonstrated track record of delivering scalable AI/ML solutions including Generative AI and agent-based systems within complex enterprise settings.