Back
Tier-1 brand, mid-level ML role, metro location and broad skillset create high candidate competition.
Specialized LLM/ML and LLMOps skills limit easy transfer across unrelated industries.
Explicit 5-8 years plus mandatory LLM, ML, and DevOps tech stack increases screening strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and develop ML pipelines and APIs for scalable model inferencing, focusing on experiment, model, and feature management.
Manage large language model (LLM) serving and training using GPU architectures and distributed training frameworks like DeepSpeed and vLLM.
Optimize model fine-tuning for latency, accuracy, and resource usage; implement DevOps and LLMOps practices involving Kubernetes, Docker, and orchestration frameworks such as Flowise and Langflow.
5-8 years of professional experience in AI/ML engineering or related roles.
Bachelor's or Master's degree in Engineering (B.Tech/M.Tech) or equivalent (MCA/BCA).
Mandatory technical skills: Gen AI, LLM, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Proficiency with cloud platforms (AWS/Azure/GCP), ML frameworks, and container orchestration tools; certifications are a bonus but not mandatory.
Experienced in full ML lifecycle management including pipeline design, model experimentation, and deployment at scale.
Strong hands-on expertise with large language models, distributed training, and optimization techniques.
Comfortable with DevOps/LLMOps environments and cloud-native technologies, able to integrate complex AI systems using modern orchestration tools.