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Tier-1 brand, mid-level generalist GenAI role, metro location, and broad toolset attract high competition.
GenAI skills are specialized to ML/AI workflows but broadly transferable across industries, giving medium background sensitivity.
Explicit 4–7 years requirement plus mandatory PyTorch, Hugging Face, cloud, LangChain, and CI/CD increases strictness.
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Design, develop, and deploy scalable generative AI solutions using large language models (LLMs) and transformer architectures.
Build and optimize model pipelines and orchestration workflows with Python, PyTorch, Hugging Face Transformers, and LangChain.
Collaborate with data engineering and MLOps teams to productionize and integrate GenAI models on cloud platforms (Azure/AWS/GCP) ensuring scalability, robustness, and compliance.
4 to 7 years of relevant work experience in generative AI engineering or similar roles.
Mandatory technical skills: Generative AI (LLMs, Transformers), Python, PyTorch, Hugging Face Transformers, cloud platforms (Azure, AWS, GCP), LangChain or similar orchestration frameworks, REST APIs (FastAPI, Flask), ML pipeline tools (MLflow, Weights & Biases), Git, and CI/CD for ML (Azure ML, SageMaker pipelines).
Education: Bachelor of Technology (B.E./B.Tech) degree or equivalent.
Work Experience Required: 4 to 7 years. Notice period: Not explicitly mentioned in the JD.
Deep technical expertise with hands-on experience building and deploying generative AI applications using LLMs and transformer models.
Experienced in cloud-native AI/ML deployment and orchestration frameworks supporting enterprise scale solutions.
Comfortable iterating on model performance using quantitative and qualitative metrics and collaborating across data engineering and MLOps teams.