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Strong brand, metro location, mid-level popular GenAI role with broad toolset increases applicant competition.
Requires specialized GenAI/LLM, PyTorch, and MLOps skills, limiting cross-industry transferability.
Explicit 4–7 years plus mandatory GenAI, PyTorch, cloud, LangChain, and ML pipeline requirements.
Design, build, and deploy scalable Generative AI solutions using large language models (LLMs) and transformer architectures.
Fine-tune foundation models with domain-specific data and optimize prompt engineering strategies for accurate model responses.
Collaborate with data engineers and MLOps to productionize AI models on cloud platforms (Azure/AWS/GCP) ensuring scalability, robustness, and compliance.
4 to 7 years of work experience in relevant AI/ML roles.
Mandatory skills: Generative AI (LLMs, Transformers), Python, PyTorch, Hugging Face Transformers, Azure/AWS/GCP cloud, LangChain or similar orchestration frameworks, REST API development (FastAPI/Flask), ML pipeline tools (MLflow, Weights & Biases), Git, and CI/CD for ML (e.g., Azure ML or SageMaker Pipelines).
Education: Bachelor of Technology (B.E/B.Tech) or equivalent (M.Tech/MCA also acceptable).
Work Experience Required: 4 to 7 years as specified in the JD. Notice period: Not explicitly mentioned in the JD.
Experienced in designing and customizing GenAI solutions with deep expertise in Python and ML frameworks such as PyTorch and Hugging Face Transformers.
Proficient in deploying and managing AI workloads on major cloud platforms with orchestration experience using LangChain or similar frameworks.
Familiar with advanced ML pipeline tools and CI/CD practices ensuring production readiness and scalability in enterprise settings.