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Tier-1 brand increases applicant density but specialized LLM/MLOps expertise narrows the qualified pool.
Advanced ML/LLM skills are transferable, but enterprise payments, governance, and production constraints raise domain sensitivity.
Extensive mandatory LLM, MLOps, productionization, tooling, and leadership requirements create strict technical filters.
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Lead ML engineering team to execute AI/ML strategy focused on business growth, customer experience, and scalable software delivery.
Design and implement multi-modal AI pipelines and multi-agent intelligence frameworks integrating diverse data types and transformer-based architectures.
Develop and productionize transformer-based models, scalable RAG systems, and end-to-end MLOps pipelines ensuring responsible AI practices and continuous innovation.
Bachelor’s or Master’s degree in Computer Science or Engineering.
Proven experience leading and managing complex AI/ML projects or products with aggressive market delivery needs.
Expert hands-on experience in designing, building, and deploying AI/ML solutions including LLM and agentic systems.
Strong programming skills in Python and SQL; familiarity with deep learning frameworks like TensorFlow or PyTorch.
Experienced technology leader capable of managing ML engineering teams and stakeholder relationships in agile, innovation-driven environments.
Strong domain expertise in advanced AI/ML techniques including transformer architectures, multi-modal data fusion, and MLOps on cloud platforms (preferably AWS).
Proficient in implementing responsible AI frameworks, integrating data ethics, bias detection, and model explainability into production systems.