Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Tier-1 brand and Bangalore location increase competition, specialised LLM requirements moderate applicant density.
Specialised LLM, GPU and distributed training expertise reduces cross-industry transferability.
Explicit 8-16 years and mandatory LLM, GPU, distributed training, cloud and MLOps skills make filters highly strict.
Design and manage ML pipelines covering experiment management, model lifecycle, feature management, and retraining, ensuring scalable API model inference.
Lead advanced LLM serving, distributed training, and optimization on GPU architectures using frameworks like DeepSpeed and vLLM to improve model latency, accuracy, and resource efficiency.
Implement DevOps and LLMOps best practices including Kubernetes, Docker, and orchestration frameworks like Flowise, Langflow to support production deployment of generative AI solutions.
8-16 years of relevant experience in data analytics and AI roles involving ML pipeline design and LLM model servicing.
Bachelor’s or Master’s degree in Engineering (B.Tech/M.Tech) or MCA/BCA in related fields.
Proven hands-on expertise with MLflow, SageMaker, Vertex AI, Azure AI, DeepSpeed, vLLM, Kubernetes, Docker, and LLM frameworks (e.g., Hugging Face, Langchain).
Proficiency in Python, SQL, and JavaScript; cloud experience with AWS, Azure, or GCP.
Experienced senior-level AI architect or lead with extensive background in operationalizing large-scale ML and LLM systems with measurable improvements in latency and accuracy.
Strong technical orientation towards distributed GPU architectures and generative AI model fine-tuning using cutting-edge toolkits and frameworks.
Comfortable working within cloud environments (AWS/Azure/GCP) and integrating DevOps and MLOps practices for robust ML lifecycle management.