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PwC brand and Bangalore metro increase applicants, but niche LLM/MLOps reduces generalist competition.
Specialized LLM, distributed training and MLOps requirements make backgrounds less transferable across industries.
Explicit 5–8 years plus mandatory LLM/MLOps, cloud and model-serving skills create strict filters.
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Design and maintain machine learning pipelines including experiment, model, and feature management, ensuring scalable model inferencing.
Lead model fine-tuning and optimization efforts to improve latency, accuracy, and resource efficiency, specifically for large language models (LLMs).
Implement and manage DevOps and LLMOps practices involving Kubernetes, Docker, and orchestration frameworks for LLM serving and distributed training.
5-8 years of relevant work experience.
Educational qualification: B.Tech, MCA, BCA, M.Tech with required degree or Master of Business Administration mentioned but inconsistent; degree required is B.Tech/MCA/BCA/M.Tech.
Mandatory skills include Generative AI, LLM, Huggingface, and Python.
Proficiency in cloud platforms (AWS, Azure, GCP), MLflow, Kubernetes, Docker, and knowledge of LLM frameworks and GPU architectures is essential.
Experienced in designing and deploying complex ML pipelines focused on advanced AI models like GPT, Llama, and other Hugging Face models.
Skilled in distributed training and model serving for large-scale LLM projects with strong DevOps/LLMOps capabilities.
Familiarity with multiple cloud ecosystems and container orchestration tools, capable of driving AI solutions end-to-end in advisory or data analytics environments.