Back
Strong Tier-1 brand and Bangalore metro increase qualified applicant density.
Highly specialized LLM, GPU, and ML pipeline expertise limits cross-industry transferability.
Explicit 8–16 years, mandatory ML/LLM, GPU, and infra skills make filters highly selective.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and implement advanced ML pipelines including experiment, model, feature management, and model retraining.
Lead deployment and scaling of large language models (LLMs) using GPU architectures and distributed training frameworks like DeepSpeed and vLLM.
Develop optimized model fine-tuning processes improving latency, accuracy, and resource efficiency; manage DevOps and LLMOps including Kubernetes, Docker, and specific orchestration frameworks.
8-16 years of work experience in relevant AI/ML roles.
Bachelor's or Master's degree in Engineering, Technology, or related fields (B.Tech, MCA, M.Tech).
Proven expertise with MLflow, SageMaker, Vertex AI, Azure AI, and proficiency in Python, SQL, and Javascript.
In-depth knowledge of GPU architectures, distributed LLM training frameworks and DevOps tools like Kubernetes and Docker.
Experienced AI architect with strong background in designing scalable ML pipelines and LLM deployment at enterprise scale.
Strong proficiency in cloud platforms AWS, Azure, GCP and managing complex AI/ML operations including model optimization.
Comfortable working in a high-travel advisory role (up to 60%) requiring hands-on expertise with cutting-edge AI technologies and frameworks.