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Tier-1 brand and Bangalore metro increase visibility, but senior niche LLM specialization moderates competition.
Highly specialized LLM, GPU, distributed training and MLOps skills limit cross-industry transferability.
Explicit 8-16 years and mandatory LLM, MLflow, distributed training, and DevOps requirements increase shortlisting strictness.
Design and manage ML pipelines including experiment, model, feature management, and retraining processes, ensuring scalable API inferencing.
Lead large language model (LLM) serving and GPU architecture tasks, including distributed training using DeepSpeed and frameworks like vLLM.
Optimize and fine-tune LLM and LVM models for improved latency, accuracy, and reduced resource usage; apply DevOps and LLMOps practices with Kubernetes, Docker, and orchestration frameworks.
8-16 years of relevant experience in AI and ML pipeline design and deployment.
Proficiency with MLflow, SageMaker, Vertex AI, Azure AI, Kubernetes, Docker, and LLM orchestration tools such as Flowise, Langflow, and Langgraph.
Strong skills in Python, SQL, JavaScript, and experience with GPU architectures and distributed training frameworks (DeepSpeed, vLLM).
B.Tech, MCA, BCA, M.Tech, Master/Bachelor of Engineering degree; relevant cloud certification(s) like AWS Solution Architect or Azure Solutions Architect preferred.
Experienced in full lifecycle ML and LLM model management and deployment, including fine-tuning and optimization for production scale.
Comfortable working across cloud platforms AWS, Azure, and GCP, integrating databases and data warehouses.
Able to operate in a complex, multi-technology environment requiring integration of DevOps, data engineering, and AI/ML frameworks for enterprise analytics.