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Tier-1 brand and Gurgaon metro raise applicant density, senior LLM specialization reduces it.
Strong LLM, MLOps, and cloud infra requirements make cross-industry transitions difficult.
Explicit 10-14 years plus mandatory LLM, MLOps, cloud and infra skills create strict filters.
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Lead and shape AI/ML initiatives, focusing on development and deployment of large language models (LLMs) and AI solutions at scale.
Design and implement microservices-based architectures using advanced software engineering and DevOps methodologies including CI/CD, container orchestration, and infrastructure as code.
Drive optimization, deployment, and monitoring of machine learning models, including expertise in LLM serving platforms, model quantization, vector databases, and MLOps practices.
10+ years of relevant experience in AI/ML or related field.
Advanced proficiency in Python and experience with LLM frameworks such as Hugging Face Transformers and LangChain.
Experience with big data processing (Spark), software engineering (microservices, concurrency), DevOps tools (Terraform, Kubernetes), and cloud platforms (AWS/GCP/Azure).
Bachelor's degree in engineering; preferred degrees include B.Tech, M.Tech, MCA, or MBA.
Senior-level AI/ML engineer with deep expertise in LLM development lifecycle including fine-tuning, evaluation, and optimization techniques like quantization and knowledge distillation.
Practical experience deploying AI/ML solutions in production via microservices and container orchestration on cloud environments with a strong focus on scalable infrastructure.
Proficiency in MLOps, including model serving, experiment tracking, CI/CD pipelines, infrastructure automation, and familiarity with vector databases and semantic search implementations.