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Tier-1 brand, metro location, and mid-level experience amplify competition despite niche LLM skill requirements.
Specialized LLM and MLOps skills transfer across industries but require specific tooling experience, so medium sensitivity.
Multiple mandatory technical skills and an explicit 3+ years requirement make shortlisting highly strict.
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Design and implement ML pipelines including experiment, model, and feature management with model retraining capabilities.
Develop scalable APIs for model inference and manage distributed training and serving of large language models (LLMs) using tools like DeepSpeed and vLLM.
Apply model fine-tuning and optimization techniques to improve latency, accuracy, and reduce resource consumption; manage LLMOps with Kubernetes, Docker, and orchestration frameworks.
Minimum 3+ years of relevant experience in Generative AI and large language model engineering.
Bachelor’s or Master’s degree in Engineering (BE/B.Tech or ME) or equivalent (MBA/MCA mentioned but Bachelor’s/Engineering emphasized).
Mandatory technical skills: Generative AI, LLM (e.g. Hugging Face), Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Not explicitly mentioned: Notice period or location restrictions; Cloud certifications are a bonus but not mandatory.
Experienced in end-to-end ML pipeline design and LLM orchestration within cloud environments (AWS, Azure, GCP).
Comfortable working with advanced LLM tools and frameworks including MLflow, SageMaker, Flowise, Langflow, and Langgraph for scalable AI model deployment.
Strong proficiency in integrating DevOps practices (Kubernetes, Docker) with machine learning operations to optimize model deployment and monitoring.