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Tier-1 brand and Bangalore metro increase applicant density despite niche GenAI specialization.
High - deep GenAI/LLM, distributed training, and MLOps expertise required, limiting cross-industry transfer.
Explicit 3+ years plus many mandatory GenAI, LLM, and MLOps technology requirements.
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Design and implement ML pipelines for experiment, model, feature management, and scalable model inferencing with tools like MLflow, SageMaker, Vertex AI, and Azure AI.
Manage distributed training and serving of large language models (LLMs), including fine-tuning and optimization to enhance latency, accuracy, and reduce resource usage, using frameworks such as DeepSpeed and vLLM.
Apply DevOps and LLMOps practices including Kubernetes, Docker, and LLM orchestration frameworks (Flowise, Langflow, Langgraph) to enable efficient large-scale AI model deployment.
Minimum 3+ years of relevant experience in Generative AI, LLMs, and associated ML pipeline development.
Mandatory skills: Generative AI, LLM, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Educational qualifications: Bachelor or Master of Engineering or equivalent degrees like BE/B.Tech/MBA/MCA.
Work Experience Required: 3+ years as explicitly mentioned.
Experienced professional with strong expertise in large language model engineering, including hands-on skills with MLflow, SageMaker, and cloud AI services across AWS, Azure, and GCP.
Comfortable working with complex distributed GPU architectures and advanced model training frameworks indicating a strategic fit for enterprise-scale AI solutions.
Operates effectively in DevOps-driven environments, leveraging container orchestration and LLMOps frameworks to enable scalable AI deployment in advisory/data & analytics contexts.