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Strong PwC brand, mid-level 4–7 years, and Bangalore metro increase applicant competition.
GenAI and LLM engineering skills are transferable across industries but require specialized ML experience.
Explicit 4–7 years plus mandatory LLM/ML, cloud, LangChain, and MLOps skills enforce strict filters.
Design, build, fine-tune, and deploy scalable Generative AI solutions using large language models (LLMs) such as OpenAI, Anthropic, Mistral, or open-source alternatives like LLaMA and Falcon.
Implement and optimize AI model pipelines with Python, PyTorch, Hugging Face Transformers, LangChain, and orchestrate deployment on cloud platforms including Azure, AWS, or GCP.
Develop APIs and integrate GenAI capabilities into enterprise systems, ensuring robustness, scalability, and compliance of AI models in production environments.
4 to 7 years of work experience in relevant AI/ML roles.
Bachelor’s degree in Engineering/Technology (B.E/B.Tech) mandatory; M.Tech/MCA preferred.
Mandatory technical skills: Generative AI (LLMs, Transformers), Python, PyTorch, Hugging Face Transformers, cloud platforms (Azure, AWS, GCP), LangChain or similar, REST APIs with FastAPI or Flask, ML pipeline tools (MLflow, Weights & Biases), Git and ML CI/CD experience (e.g., Azure ML, SageMaker pipelines).
Work Experience Required: 4 to 7 years. Notice period: Not explicitly mentioned in the JD.
Experienced in deploying GenAI models end-to-end in cloud environments with a strong focus on production scalability and compliance.
Comfortable collaborating with data engineering and MLOps teams to operationalize model workflows using orchestration frameworks.
Hands-on with both AI model development (fine-tuning, prompt engineering) and integration into enterprise-grade applications leveraging APIs and pipeline automation.