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Strong Tier-1 brand, metro location, and mid-level 3+ AI role attract dense applicant competition.
Specialized GenAI and LLMops skills are transferable across industries but require ML/AI domain experience.
Explicit 3+ years plus mandatory GenAI, LLM, MLops, and GPU/DeepSpeed expertise increases shortlisting strictness.
Design and manage ML pipelines including experiment, model, and feature management plus model retraining.
Lead design and deployment of scalable model inference APIs with expertise in Gen AI frameworks and cloud AI platforms (MLflow, SageMaker, Vertex AI, Azure AI).
Fine-tune, optimize, and deploy large language models (LLMs) with deep domain knowledge of GPU architectures, distributed training, and LLM orchestration frameworks.
Minimum 3+ years of relevant experience in Gen AI, LLM development, model fine-tuning, and ML Ops.
Mandatory skills: Generative AI, LLM, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Bachelor's degree in Engineering (BE/B.Tech) or equivalent; Master’s preferred.
Work Experience Required: 3+ years
Experienced in advanced ML pipeline architecture and operationalization of large-scale AI/ML models in cloud environments (AWS, Azure, GCP).
Deep technical expertise in distributed LLM training and serving using frameworks such as DeepSpeed and vLLM, and in AI DevOps including Kubernetes and container orchestration.
Proficient in integrating multiple AI tools and cloud services to optimize model performance and resource utilization.