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Tier-1 employer, Bangalore location, mid-level (3+ years) role increases candidate density despite niche GenAI skills.
Requires specialized GenAI, LLM, and LLMOps/cloud expertise, limiting cross-industry transferability.
Multiple mandatory GenAI/LLM technologies plus explicit 3+ years requirement enforce strict screening.
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Design and manage ML pipelines including experiment, model, and feature management, as well as model retraining and API deployment at scale.
Implement distributed training and serving of large language models (LLMs) leveraging GPU architectures and frameworks like DeepSpeed and vLLM.
Optimize model fine-tuning for latency, accuracy, and resource efficiency; manage DevOps and LLMOps with Kubernetes, Docker, and orchestration frameworks like Flowise, Langflow, and Langgraph.
Minimum 3 years of relevant work experience in Generative AI and LLMs.
Mandatory skills: Gen AI, LLM, Hugging Face, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Education: Bachelor or Master of Engineering (BE/B.Tech or ME preferred) or MBA/MCA with relevant technical focus.
Proficient in Python, SQL, and JavaScript; experience with cloud platforms AWS, Azure, or GCP; DevOps knowledge with container orchestration required.
Experienced in end-to-end AI/ML lifecycle management with hands-on expertise in designing scalable ML pipelines and APIs for AI model deployment.
Strong background in advanced LLM technologies, including distributed training and GPU optimization for large-scale AI models.
Comfortable working with cloud-native DevOps tools and orchestration frameworks to enable robust LLMOps practices in agile advisory environments.