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Tier-1 brand, metro location, mid-level experience, and popular GenAI role increase applicant competition.
Technical LLMOps and ML engineering skills transfer across industries but require specialized ML/LLM experience.
Mandatory GenAI/LLM, PyTorch/TensorFlow, Langchain, Docker, Kubernetes and explicit 3+ years raise shortlisting strictness.
Design and manage ML pipelines including experiment, model, and feature management with tools like MLflow, SageMaker, Vertex AI, and Azure AI.
Handle deployment and scaling of large language models (LLMs) including distributed training using frameworks such as DeepSpeed and service frameworks like vLLM.
Optimize and fine-tune LLMs to improve latency and accuracy while reducing resource requirements; maintain DevOps and LLMOps processes using Kubernetes, Docker, and orchestration frameworks.
Minimum 3+ years of work experience in relevant AI/ML roles.
Mandatory skills: Generative AI, LLMs, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Educational qualification: Bachelor or Master of Engineering, or MBA/MCA.
Work Experience Required: 3+ years.
Proven experience in designing and managing ML pipelines and LLM deployment at scale in cloud environments (AWS/Azure/GCP).
Strong expertise in model fine-tuning, optimization, and distributed training frameworks specific to large language models.
Hands-on knowledge of DevOps practices with containerization and orchestration tools to maintain scalable AI model infrastructure.