Back
Tier-1 brand, mid-level role, metro location, and broad tooling/skill requirements heighten competition.
Highly specialized LLM, GPU, and MLOps skills reduce cross-industry transferability.
Explicit 5–8 years plus mandatory LLM, MLOps, GPU and specific tooling creates strict filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and manage ML pipelines including experiment, model, feature management, and retraining with tools like MLflow, SageMaker, Vertex AI, and Azure AI.
Develop scalable APIs for model inference, focusing on large language model serving and optimization, including distributed training using DeepSpeed and vLLM.
Implement DevOps and LLMOps practices involving Kubernetes, Docker, container orchestration, and orchestration frameworks like Flowise, Langflow, and Langgraph.
5-8 years of relevant experience in AI/ML engineering with emphasis on Gen AI, LLM, and Huggingface.
Educational qualification: B.Tech/MCA/BCA/M.Tech; Mention of MBA degree but unclear if mandatory or preferred.
Proficiency in Python is mandatory; familiarity with JavaScript and JSON is expected.
Experience with cloud platforms (AWS, Azure, GCP) and container orchestration (Kubernetes, Docker).
Experienced in designing and deploying production-grade ML pipelines and APIs for large language models at scale.
Strong hands-on knowledge of GPU architectures for distributed training and serving of LLMs with optimization skills to improve latency and accuracy.
Demonstrated expertise in DevOps/LLMOps frameworks and cloud-native technologies aligned with modern AI operational workflows.