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PwC brand, metro location, and mid-level experience increase competition despite niche GenAI specialization.
Generative AI engineering skills transfer across industries but require specific ML/LLM expertise, so medium sensitivity.
Explicit 4–7 year requirement plus mandatory LLM, PyTorch, LangChain, cloud and MLOps skills raises filter 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 using Python, PyTorch, Hugging Face Transformers, and orchestration frameworks like LangChain.
Collaborate with data engineering and MLOps teams to productionize models on cloud platforms (Azure, AWS, GCP) ensuring robustness and scalability.
4 to 7 years of work experience in software development or AI engineering roles.
Bachelor of Technology (B.E./B.Tech) or equivalent degree required; M.Tech/MCA also acceptable.
Proficiency in Python, PyTorch, Hugging Face Transformers and cloud deployment on Azure/AWS/GCP.
Experience with orchestration frameworks (LangChain), ML pipelines (MLflow, Weights & Biases), REST APIs (FastAPI or Flask), and CI/CD for ML.
Experienced in deploying generative AI using large language models with domain-specific fine-tuning experience.
Hands-on with AI orchestration, cloud AI platforms (Azure AI Foundry, GCP Vertex AI), and modern MLOps tools.
Able to integrate GenAI capabilities into enterprise applications and optimize prompt engineering for accurate model outputs.