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Strong PwC brand and Bangalore metro increase competition, though GenAI specialization narrows applicant pool.
Specialized GenAI/ML skills are transferable across industries but require technical depth.
Explicit 4–7 years plus mandatory GenAI tech stack and cloud/MLOps requirements raise strictness.
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Design, develop, and deploy scalable Generative AI solutions using large language models (LLMs) and transformer architectures with Python, PyTorch, and Hugging Face.
Build and integrate GenAI capabilities into enterprise systems including API development and ML pipeline management using tools like LangChain, MLflow, and cloud platforms (Azure, AWS, GCP).
Ensure robustness, scalability, and compliance of AI models while collaborating with data engineers and MLOps teams for production deployment.
4 to 7 years of professional experience in related AI/ML roles.
Bachelor's degree in Technology (B.E/B.Tech) or equivalent; M.Tech or MCA also acceptable.
Proficiency in Generative AI (LLMs, Transformers), Python, PyTorch, Hugging Face Transformers, and cloud platforms (Azure/AWS/GCP).
Experience with ML orchestration frameworks (LangChain/Langgraph), REST APIs (FastAPI/Flask), ML pipeline tools (MLflow, Weights & Biases), and CI/CD for ML (Azure ML, SageMaker pipelines).
Experienced in fine-tuning and customizing foundation models using domain-specific datasets and prompt engineering.
Comfortable working in a cloud environment integrating AI models for enterprise-scale deployment and collaborating with cross-functional teams (data engineers, MLOps).
Skilled in developing end-to-end Generative AI workflows including API integration, model evaluation, and iterative experimentation on platforms like Azure, AWS, or GCP.