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PwC brand, Bangalore location, mid-level (4–7 yrs) and broad GenAI/MLOps skills increase candidate competition.
Specialized GenAI and MLOps skills moderately limit cross-industry transferability.
Explicit 4–7 years plus mandatory GenAI, PyTorch, cloud and MLOps skills enforce stringent shortlisting.
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Design, develop, and deploy scalable Generative AI solutions using large language models (LLMs) including fine-tuning and prompt engineering.
Build and orchestrate model workflows with Python, PyTorch, Hugging Face Transformers, LangChain and integrate GenAI capabilities into enterprise applications via APIs.
Collaborate with data engineering and MLOps teams to productionize AI models on cloud platforms (Azure, AWS, GCP), ensuring robustness, scalability, and compliance.
4 to 7 years of experience in Generative AI engineering or related roles.
Mandatory skills: Python, PyTorch, Hugging Face Transformers, experience with cloud AI platforms (Azure, AWS, GCP), LangChain or similar orchestration frameworks, ML pipeline tools (MLflow, Weights & Biases), and CI/CD practices for ML.
Education: Bachelor of Technology, Bachelor of Engineering, M.Tech, or MCA degree.
Work Experience Required: 4 to 7 years (explicitly mentioned).
Experienced in designing end-to-end GenAI solutions incorporating recent foundation models and cutting-edge open-source tools.
Operationally skilled in managing ML pipelines, CI/CD, and cloud deployment environments for AI workloads.
Familiarity with both production-grade AI model deployment and experimental fine-tuning or prompt optimization workflows.