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Tier-1 employer attracts applicants, but seniority and niche ML/AI specialization reduce candidate density.
Advanced ML/AI leadership skills are transferable across industries though generative AI experience increases domain sensitivity.
Explicit 12–16 years plus specialized ML/AI, MLOps, and leadership requirements enforce strict shortlisting.
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Lead and scale a team focused on data science, machine learning, generative AI, and agentic AI systems.
Define and drive AI/ML strategy aligned with business goals, overseeing full ML lifecycle and operationalizing AI solutions with measurable business impact.
Collaborate across functions to integrate AI/ML into production environments, implementing best practices in MLOps, model governance, and ethical AI.
12 to 16 years of professional experience.
Strong expertise in Data Science, Machine Learning, AI including generative AI models (e.g., GPT, BERT, DALL-E) and agentic AI concepts such as reinforcement learning and autonomous systems.
Proficiency with ML frameworks (TensorFlow, PyTorch, Scikit-learn, Hugging Face, LangChain) and programming in Python.
Graduate Engineer or Management Graduate (MBA or Bachelor of Engineering).
Experienced in managing and mentoring data science or AI/ML teams within agile, cross-functional environments.
Demonstrates ability to translate complex AI technologies into business value and actionable insights.
Knowledgeable in state-of-the-art AI research, MLOps, AI ethics, and cloud AI platforms (Azure ML, AWS SageMaker, Google Vertex AI).