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Strong PwC brand, metro Bangalore, mid-level GenAI role attracting many applicants.
Specialized GenAI, LLMOps and distributed training skills reduce transferability across industries.
Explicit 3+ years plus mandatory LLM, DeepSpeed, HuggingFace, Kubernetes and cloud skills make filters strict.
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Design and implement machine learning pipelines including experiment, model, and feature management, plus model retraining and scalable model inferencing APIs.
Lead distributed training and serving of large language models using GPU architectures and frameworks such as DeepSpeed and vLLM.
Optimize model fine-tuning to improve latency, accuracy, and reduce resource usage for LLM and LVM models, leveraging DevOps and LLMOps tools and cloud platforms.
Minimum 3+ years of relevant experience in Generative AI, LLMs, and ML pipeline design.
Mandatory skills: Gen AI, LLM, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Education: Bachelor’s degree in Engineering (BE/B.Tech) or Master’s degree in Engineering (M.E/M.Tech/MCA/MBA).
Proficiency in DevOps tools (Kubernetes, Docker) and cloud platforms (AWS, Azure, GCP) explicitly required.
Experienced in building and managing ML ops pipelines for large-scale LLM deployment with expertise in distributed GPU training architectures.
Strong hands-on expertise with LLM orchestration frameworks (Flowise, Langflow, Langgraph) and integrating cloud-based AI services (SageMaker, Vertex AI, Azure AI).
Technical proficiency across Python, cloud services, container orchestration, and LLM fine-tuning to deliver optimized AI solutions at scale.