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Strong firm brand, mid-level experience band, and Bangalore location increase competition despite niche GenAI specialization.
GenAI and MLOps expertise is transferable across industries but requires deep specialized experience.
Mandatory 4–7 years plus specific LLM, PyTorch, Hugging Face, cloud, LangChain, and CI/CD skills enforce high strictness.
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Design, develop, and deploy scalable Generative AI solutions using large language models and transformer architectures.
Build and optimize AI model pipelines with Python, PyTorch, Hugging Face Transformers, and orchestration frameworks such as LangChain.
Collaborate with data engineers and MLOps teams to productionize AI models on cloud platforms (Azure, AWS, GCP) ensuring robustness and scalability.
4 to 7 years of relevant work experience.
Bachelor of Technology (B.E/B.Tech), M.Tech, or MCA degree mandatory.
Proficiency in Python, PyTorch, Hugging Face Transformers, Azure/AWS/GCP cloud platforms, LangChain or similar orchestration frameworks, REST APIs (FastAPI or Flask), and ML pipeline tools (MLflow, Weights & Biases).
Experience with CI/CD for ML using platforms like Azure ML or SageMaker Pipelines required.
Experienced in designing end-to-end Generative AI solutions including fine-tuning foundation models and prompt engineering.
Hands-on expertise with cloud AI platforms and collaboration with cross-functional teams (data engineers, MLOps).
Capable of integrating GenAI capabilities into enterprise applications and maintaining compliance and scalability in production environments.