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PwC brand and Bangalore metro increase applicant density, but senior specialized LLM skills narrow the pool.
Deep LLM, MLOps, and cloud infrastructure expertise required, limiting cross-industry transferability.
Mandatory senior experience and extensive LLM, MLOps, cloud, and infra requirements create strict filtering.
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Lead design, development, and deployment of advanced AI/ML solutions, including large language models (LLMs) and associated infrastructure.
Own AI/ML project lifecycle with responsibilities ranging from model optimization, feature engineering, to deployment and monitoring using MLOps practices.
Drive scalable architecture using microservices, containerization (Kubernetes), and cloud platforms (AWS/GCP/Azure) to support AI/ML initiatives.
8-12 years of professional experience with at least 4 years specifically in AI/ML or related roles.
Strong expertise in Python programming, experience with LLM frameworks (e.g., Hugging Face Transformers, LangChain) and prompt engineering.
Experience in microservices architecture, DevOps tools (Terraform, CI/CD pipelines, Kubernetes), and MLOps tools (MLflow, TorchServe/TF Serving).
Educational qualification: Bachelor/Master of Engineering, B.Tech/M.Tech, or MBA.
Experienced in implementing LLM-based applications such as chatbots, recommendation systems, and conversational AI using vector databases and retrieval-augmented generation (RAG).
Proficient in software engineering best practices including test-driven development and concurrency, enabling robust and scalable AI solutions.
Demonstrated ability to architect and manage AI/ML infrastructure on cloud environments, with a strong focus on model optimization, deployment automation, and monitoring.