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Tier‑1 brand, metro location, and mid‑level GenAI/ML skillset increase candidate competition.
Specialized LLM, MLOps, and GPU expertise limits cross‑industry transferability, increasing domain sensitivity.
Multiple mandatory GenAI, LLM, MLOps, GPU and 3+ years requirements make filters very strict.
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Design and manage ML pipelines for model experimentation, feature management, and scalable model inferencing using tools like MLflow, SageMaker, and Azure AI.
Implement distributed training and serving of large language models with expertise in GPU architectures and frameworks such as DeepSpeed and vLLM.
Lead model fine-tuning and optimization to improve latency and accuracy while reducing resource usage; leverage DevOps and LLMOps tools including Kubernetes, Docker, and orchestration frameworks like Flowise and Langflow.
Minimum 3 years of relevant experience in Generative AI and Large Language Models (LLM).
Proficient in Python, Huggingface, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, and Kubernetes.
Bachelor’s or Master’s degree in Engineering (BE/B.Tech/ME), MBA, or MCA.
Work Experience Required: 3+ years
Experienced in advanced ML deployment and LLM operationalization, capable of handling end-to-end ML pipelines and distributed model training.
Skilled in cloud platforms (AWS, Azure, GCP) and DevOps practices for scalable AI model management.
Familiar with LLM orchestration frameworks and multiple data stores, comfortable working in advisory/data and analytics environments.