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PwC brand, mid-level 4–7yrs, Bangalore metro, and strong GenAI demand increase candidate competition.
Core GenAI engineering skills are transferable, but enterprise consulting and integration needs raise industry specificity.
Multiple mandatory skills and explicit 4–7 years requirement increase screening rigor.
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Design, build, and deploy scalable Generative AI solutions using LLMs and transformer architectures (OpenAI, Anthropic, Mistral, LLaMA, Falcon).
Fine-tune foundation models with domain-specific data and develop prompt engineering strategies for context-aware responses.
Collaborate with data engineers and MLOps teams to productionize GenAI models on Azure, AWS, or GCP ensuring robustness, scalability, compliance, and integration into enterprise applications via APIs.
4 to 7 years of work experience in relevant fields.
Mandatory skills: Python, PyTorch, Hugging Face Transformers, experience with Azure/AWS/GCP cloud platforms.
Experience with orchestration frameworks like LangChain, REST API development (FastAPI or Flask), and ML pipeline tools (MLflow, Weights & Biases).
Familiarity with Git and CI/CD for ML deployment (e.g. Azure ML, SageMaker Pipelines).
Degree required: Bachelor of Technology (B.E./B.Tech) or equivalent.
Experienced in end-to-end productionization of Generative AI models and integrating them into enterprise-scale applications.
Proficient in cloud-native AI platforms and orchestration frameworks for scalable and robust deployment.
Capable of iterative model evaluation and optimization using quantitative and qualitative metrics, with a focus on domain-specific customization.