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PwC brand, mid-level experience requirement, Bangalore metro increases applicant density despite niche GenAI specialization.
Highly specialized GenAI/LLM and MLOps skills reduce cross-industry transferability.
Explicit 3+ years and many mandatory GenAI, LLM, and MLOps technical requirements.
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Design and manage machine learning pipelines including experiment, model, and feature management plus model retraining and API development for scalable model inferencing.
Develop and optimize large language model (LLM) training and serving architectures using GPU frameworks and distributed training technologies like DeepSpeed and vLLM.
Apply DevOps and LLMOps practices using Kubernetes, Docker, and LLM orchestration frameworks such as Flowise, Langflow, and Langgraph to support ML deployment and operations.
Minimum 3+ years of professional experience in generative AI, LLM development, and ML pipeline engineering.
Mandatory technical skills: Generative AI, LLM (Huggingface, GPT, Llama), Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Educational qualification: Bachelor of Engineering or Master of Engineering degree; MBA or MCA also mentioned but primary focus on engineering degrees.
Work Experience Required: 3+ years in relevant AI/ML roles.
Experienced in designing and deploying scalable ML workflows with focus on operationalizing large language models in cloud environments (AWS, Azure, GCP).
Strong hands-on expertise in both AI model management and DevOps, including knowledge of LLM orchestration tools and GPU-based distributed training frameworks.
Comfortable working with multiple cloud platforms and data storage technologies, demonstrating ability to optimize model performance and resource efficiency under production constraints.