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Tier-1 brand, mid-level experience, metro location, and broad skill requirements drive high competition.
Specialized LLM, GPU/distributed training and MLOps expertise limits cross-industry transferability.
Mandatory 3+ years plus specific GenAI, LLM, PyTorch, HuggingFace, Kubernetes and MLOps skills create strict filters.
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Design and manage machine learning pipelines including experiment, model, feature management, and retraining, leveraging platforms like MLflow, SageMaker, Vertex AI, and Azure AI.
Implement and optimize distributed training and serving of large language models (LLMs) using DeepSpeed, vLLM, and expertise in GPU architectures to improve latency, accuracy, and reduce resource use.
Apply DevOps and LLMOps practices with Kubernetes, Docker, and orchestration frameworks such as Flowise, Langflow, and Langgraph for scalable AI model deployment.
Minimum 3+ years of work experience in relevant AI and ML roles.
Mandatory skills: Generative AI, Large Language Models (LLM), Hugging Face, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Educational qualification: Bachelor of Engineering or Master of Engineering degrees in relevant fields; MBA or MCA also acceptable.
Not explicitly mentioned: Notice period or strict location constraints beyond Bangalore.
Experienced in advanced ML pipeline design and large-scale ML model deployment in cloud environments (AWS, Azure, GCP).
Strong focus on Gen AI and LLM technologies with hands-on proficiency in fine-tuning and optimization at scale for business applications.
Comfortable working with modern DevOps, container orchestration tools, and AI model operationalization frameworks indicating strategic and technical integration skills.