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Tier-1 brand, sought-after ML/LLM role, and metro location increase candidate competition.
Specialized LLM and MLOps expertise is moderately transferable across industries but still domain-specific.
Explicit 10–14 year requirement and many mandatory ML/MLOps skills make filters strict.
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Lead and shape AI/ML initiatives with a focus on developing and deploying advanced AI and ML solutions using Python, LLM frameworks (Hugging Face Transformers, LangChain), and big data technologies (Spark).
Design, implement, and optimize microservices-based architectures, including MLOps practices, containerization, and cloud infrastructure (AWS/GCP/Azure) for scalable AI/ML deployments.
Drive LLM infrastructure and deployment, including model serving, quantization, vector database management, and production-grade software engineering with DevOps tools and practices.
10-14 years of relevant experience in AI/ML or related fields focusing on large language models and data science.
Strong proficiency in Python, experience with LLM frameworks, big data processing (Spark), microservices architecture, DevOps (Terraform, CI/CD, Kubernetes) and cloud platforms (AWS, GCP, Azure).
Bachelor’s degree in Engineering (B.Tech) required; M.Tech/MCA/MBA are preferred.
Work Location: Gurugram; hybrid work environment; No mention of notice period, certifications, or visa sponsorship requirements.
Senior-level technologist experienced in end-to-end AI/ML lifecycle for enterprise-scale LLM applications including chatbots, recommendation systems, and semantic search.
Operates effectively within microservices and cloud-native environments, with advanced software engineering and MLOps skills.
Strong focus on data-driven AI product optimization, integrating model quantization, experimental design (A/B testing), and vector database management for scalable AI solutions.