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Tier-1 brand, metro location and early-mid level attract moderate competition.
Requires specialized LLM, ML and cloud tooling expertise, limiting cross-industry transferability.
Explicit 3+ years and numerous mandatory GenAI/LLM and cloud tooling requirements increase selection strictness.
Design, implement, and manage machine learning pipelines including experiment, model, and feature management, and model retraining.
Develop and optimize APIs and serve large language models (LLMs) leveraging GPU architectures and distributed training frameworks like DeepSpeed and vLLM.
Lead DevOps and LLMOps practices involving container orchestration (Kubernetes, Docker) and LLM orchestration frameworks (Flowise, Langflow, Langgraph).
Minimum 3 years of relevant work experience in generative AI, LLMs, and associated technologies.
Mandatory skills: Generative AI, LLM, Hugging Face, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Education: Bachelor or Master of Engineering (B.E./M.E.) or equivalent degrees such as B.Tech, MBA, MCA.
Work visa sponsorship: Not available; Notice period and travel requirements: Not explicitly mentioned.
Experienced in applying advanced ML and LLM frameworks in production environments emphasizing model optimization and latency reduction.
Proficient in cloud platforms (AWS, Azure, GCP) and skilled in DevOps practices with container orchestration.
Strong expertise with LLM ecosystems including Hugging Face, Langchain, Langgraph plus familiarity with databases and data warehouses for scalable solutions.