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Tier-1 brand, mid-level AI role, and metro Bangalore location drive high applicant competition.
LLM and MLOps skills transfer across industries but require specialized AI experience, so moderate sensitivity.
Explicit 5–8 years plus mandatory LLM and MLOps stack enforces strict technical filtering.
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Design and manage ML pipelines including experiment management, model retraining, and scalable inferencing APIs.
Develop, fine-tune, and optimize large language models (LLMs) leveraging GPU architectures and distributed training frameworks like DeepSpeed and vLLM.
Implement DevOps and MLOps practices using Kubernetes, Docker, and LLM orchestration frameworks to deploy and maintain AI models efficiently.
5-8 years of work experience in relevant fields.
Proficiency in Gen AI, LLM, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Educational qualification: B.Tech, MCA, BCA, or M.Tech in Engineering or Computer Science.
Experience with cloud platforms (AWS, Azure, GCP) and container orchestration is mandatory.
Experienced AI engineer with deep expertise in large language models and advanced ML pipeline design.
Skilled in deploying and managing AI models in distributed and cloud environments using DevOps and MLOps frameworks.
Comfortable translating complex business problems into data science solutions and interfacing effectively with technical and non-technical stakeholders.