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 metro location increase competition, but specialized LLM/MLops skillset reduces applicant density.
Specialized LLM, GPU, and MLOps expertise creates high industry specificity and limited transferability.
Explicit 5-8 years plus many mandatory LLM, MLOps, and infrastructure skills tighten shortlisting.
Design and manage ML pipelines including experiment, model, feature management, and model retraining using tools like MLflow, SageMaker, Vertex AI, and Azure AI.
Develop APIs for scalable model inferencing and optimize large language model (LLM) training and serving leveraging GPU architectures and distributed training frameworks such as DeepSpeed and vLLM.
Implement DevOps and LLMOps practices involving Kubernetes, Docker, and LLM orchestration frameworks including Flowise, Langflow, and Langgraph.
5-8 years of relevant work experience.
Proficiency in Gen AI, LLM, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Bachelor’s or Master’s degree in Engineering or equivalent (B.Tech/M.Tech/MCA/BCA).
Work Visa Sponsorship: Not available.
Experienced AI Engineer with strong expertise in generative AI and large language model ecosystems involving Huggingface, GPT, and related platforms.
Demonstrates hands-on skills in building and optimizing ML pipelines and LLM fine-tuning in cloud environments (AWS, Azure, GCP).
Operates effectively with DevOps and container orchestration tools in a complex, scalable AI solution environment.