Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Tier-1 brand, metro location, and 3+ year mid-level band present, but niche LLM skillset narrows competition.
Highly specialized LLM/LLMOps and distributed training skills limit cross-industry transferability.
Role requires explicit technical LLM, GPU, cloud, and tooling expertise plus a minimum experience requirement.
Design and manage machine learning pipelines including experiment, model, and feature management, as well as model retraining and inferencing at scale.
Develop and optimize large language model (LLM) serving architectures with distributed training using frameworks like DeepSpeed and vLLM to enhance latency, accuracy, and resource efficiency.
Implement DevOps and LLMOps practices involving Kubernetes, Docker, and orchestration frameworks (Flowise, Langflow, Langgraph) to support scalable AI deployments.
3+ years of relevant experience working with Generative AI, LLM, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, and Kubernetes.
Bachelor’s or Master’s degree in Engineering (BE/B.Tech or ME) or related fields such as MBA or MCA.
Proficiency in Python, SQL, and Javascript required.
Work Experience Required: 3+ years.
Experienced in advanced ML pipeline design and management with knowledge of MLflow, SageMaker, Vertex AI, and Azure AI platforms.
Strong understanding and practical application of large language models, including fine-tuning and distributed training techniques.
Skilled in DevOps for AI workflows, comfortable working with containerization, orchestration, and multiple cloud environments (AWS, Azure, GCP).