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Tier-2 Big Four brand, metro location, mid-level experience yet niche LLM skillset limits broad applicant competition.
Highly specialized LLM and GPU/MLops requirements reduce cross-industry transferability.
Multiple mandatory LLM, MLOps, cloud, and years requirements enforce stringent technical filters.
Design and manage machine learning pipelines including experiment, model, and feature management, along with building scalable model inference APIs.
Lead large language model (LLM) serving and GPU architecture optimization focusing on distributed training with frameworks like DeepSpeed and vLLM.
Implement model fine-tuning and optimization to improve latency, accuracy, and reduce training/resource consumption; apply DevOps and LLMOps practices using Kubernetes, Docker, and orchestration frameworks.
Minimum 3+ years of relevant experience in Generative AI, LLM, and machine learning pipeline design.
Must have proficiency in Gen AI technologies including Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, and Kubernetes.
Educational qualification: Bachelor or Master of Engineering, or equivalent degree (BE/B.Tech preferred).
Mandatory skills include expertise in Python, SQL, JavaScript, and cloud platforms AWS/Azure/GCP; certifications are a bonus but not mandatory.
Experience working on advanced AI/ML engineering projects involving LLMs and GPU distributed training in cloud environments.
Operational expertise in MLOps/LLMOps and deploying scalable AI solutions using container orchestration and cloud-native services.
Technical depth in both model development and infrastructure automation, suited for a Senior Associate in a data and analytics advisory role.