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Strong PwC brand increases applicant density but seniority and niche LLM skillset moderate competition.
Highly domain-specific LLM, GPU, and MLOps requirements limit cross-industry transferability.
Explicit 11–15 years and mandatory GenAI, LLM, MLOps, cloud, and GPU expertise enforce strict filters.
Lead design and management of end-to-end Gen AI/LLM pipelines including experiment, model, feature management, and retraining automation.
Develop scalable APIs for model inferencing and optimize large language models for latency, accuracy, and resource efficiency.
Oversee deployment and serving of LLMs on GPU architectures using Kubernetes, Docker, and LLM orchestration frameworks like Flowise and Langflow.
11-15 years of relevant work experience in AI/ML architecture or related data and analytics roles.
Mandatory skills: Gen AI, LLM, Hugging Face, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Bachelor of Technology degree required; M.Tech, MBA, or MCA are additional qualifications mentioned.
Work Experience Required: 11-15 years; Notice Period: Not explicitly mentioned in the JD.
Experienced in deploying and managing machine learning models at scale, particularly large language models, with a strong focus on model optimization and DevOps integration.
Strong hands-on expertise in cloud platforms (AWS, Azure, GCP) and container orchestration tools supporting AI workloads.
Comfortable working in complex, technology-driven advisory environments focusing on data accuracy, management, and innovative AI solutions.