Back
Tier-1 brand, metro location and mid-level experience increase competition, but niche GenAI/LLM skillset limits applicant pool.
Role requires specialized LLM, GPU, and LLMOps expertise, limiting cross-industry transferability.
Mandatory 3+ years plus specific GenAI/LLM, cloud and DevOps tech stacks create stringent shortlisting 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, and feature management, plus model retraining and API development for scaling inferencing.
Lead distributed training and serving of large language models leveraging deep expertise in GPU architectures and frameworks such as DeepSpeed and vLLM.
Optimize model fine-tuning to improve latency and accuracy while reducing training time and resource use; apply DevOps and LLMOps expertise with tools like Kubernetes, Docker, and orchestration frameworks.
3+ years of relevant experience in Generative AI, LLMs, and ML pipeline design.
Mandatory skills include: Gen AI, LLM, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, and Kubernetes.
Educational qualification: Bachelor or Master of Engineering, B.Tech, MBA, or MCA.
Work Experience Required: Minimum 3 years as explicitly mentioned.
Strong background in designing scalable ML pipelines and performing distributed LLM training and serving in cloud environments (AWS, Azure, GCP).
Experienced in LLMOps, DevOps, and container orchestration with deep technical skills in GPU architectures and modern LLM orchestration tools.
Proficient in Python, SQL, and cloud-native AI services with strategic understanding of fine-tuning models to optimize performance and cost.