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Tier-1 brand, mid-level 4–7 years, Bangalore metro, and broad GenAI skill requirements raise competition.
Highly specialized GenAI, model fine-tuning, and MLOps requirements make cross-industry moves difficult.
Explicit 4–7 years plus mandatory LLM, PyTorch, LangChain, cloud and ML pipeline skills create strict filters.
Design, develop, and deploy scalable Generative AI solutions using state-of-the-art large language models and transformer architectures.
Build and optimize AI model pipelines using Python, PyTorch, Hugging Face Transformers, LangChain, and integrate GenAI capabilities into enterprise applications via APIs.
Collaborate with data engineers and MLOps teams to productionize and ensure scalability, robustness, and compliance of AI models on cloud platforms (Azure/AWS/GCP).
4 to 7 years of work experience.
Bachelor's degree required (B.E/B.Tech/M.Tech/MCA).
Mandatory skills: Generative AI (LLMs, Transformers), Python, PyTorch, Hugging Face Transformers, Azure/AWS/GCP cloud platforms, LangChain or similar orchestration frameworks, REST APIs development (FastAPI, Flask), ML pipeline tools (MLflow, Weights & Biases), Git, and CI/CD for ML (e.g., Azure ML, SageMaker pipelines).
Work Experience Required: 4 to 7 years
Experienced in deploying and fine-tuning foundation models within enterprise environments using cloud AI platforms like Azure AI Foundry or GCP Vertex.
Proficient in orchestration frameworks (LangChain or equivalent) and managing ML workflows integrating multiple tools and APIs.
Ability to implement agentic AI and maintain production-ready GenAI solutions focusing on scalability, robustness, and compliance.