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Tier-1 brand, mid-level generalist ML role, metro location increase applicant competition.
Requires specialized LLM and cloud engineering skills, moderately transferable across industries.
Explicit 3+ years plus mandatory LLM, PyTorch, Langchain, Kubernetes and cloud skills increase filtering strictness.
Design and manage machine learning pipelines including experiment, model, and feature management and API inferencing at scale.
Implement distributed training and serving of large language models (LLMs) with expertise in GPU architectures and frameworks like DeepSpeed and vLLM.
Lead model fine-tuning, optimization, and DevOps/LLMOps practices using Kubernetes, Docker, and orchestration tools such as Flowise, Langflow, and Langgraph.
Minimum 3+ years of professional experience in relevant roles.
Mandatory skills: Generative AI, LLM, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Educational qualification: Bachelor of Engineering or Master of Engineering (BE/B.Tech or equivalent).
Work Experience Required: 3+ years explicitly mentioned.
Experienced in deploying and optimizing LLMs with strong proficiency in distributed training and GPU architectures.
Hands-on with ML engineering tools and cloud AI platforms (AWS SageMaker, Google Vertex AI, Azure AI).
Capable of implementing advanced DevOps and LLMOps workflows for scalable AI-driven applications.