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Strong Tier-1 brand, metro location, and attractive 3+ mid-level role balanced by niche GenAI specialization.
Highly specialized LLM, GPU, and distributed-training skills limit transferability across industries.
Multiple mandatory GenAI, LLM, ML frameworks, DevOps and Kubernetes requirements plus explicit years.
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Design and manage ML pipelines including experiment, model, and feature management and model retraining using tools like MLflow, SageMaker, Vertex AI, and Azure AI.
Develop and optimize large language model (LLM) deployment and fine-tuning with expertise in GPU architectures, distributed training frameworks such as DeepSpeed, and LLM orchestration tools including Flowise, Langflow, and Langgraph.
Implement DevOps and LLMOps practices leveraging Kubernetes, Docker, container orchestration, and cloud platforms to improve model inference scalability, latency, and resource efficiency.
Minimum 3+ years relevant work experience in Generative AI, LLM, and advanced ML pipeline development.
Bachelor of Engineering or Master of Engineering degree mandatory; MBA/MCA also mentioned but degree requirement explicit for BE/MEng.
Mandatory skills: Generative AI, LLM, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Not explicitly mentioned in the JD: Notice period, work location constraints, visa sponsorship details.
Experienced in handling end-to-end ML lifecycle management with a focus on large language models and generative AI applications.
Technically proficient in both cloud environments (AWS, Azure, GCP) and container orchestration with strong DevOps/LLMOps specialization.
Demonstrates practical expertise in improving model performance and resource optimization for LLM serving and fine-tuning at scale.