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PwC brand, mid-level GenAI role in Bangalore with broad ML/LLM requirements increases competition, though skills are specialized.
GenAI engineering skills are transferable across industries but require domain-specific ML and platform experience.
Explicit 4–7 years plus mandatory LLM, PyTorch, cloud, and MLOps skills enforce strict filtering.
Design, develop, and deploy scalable generative AI solutions using LLMs and transformer architectures.
Implement and optimize AI model pipelines leveraging Python, PyTorch, Hugging Face, LangChain, and cloud platforms (Azure, AWS, GCP).
Integrate GenAI capabilities into enterprise applications via APIs, ensuring robustness, scalability, and compliance.
4 to 7 years of work experience in relevant AI/ML roles.
Bachelor of Technology (B.E./B.Tech) degree required; M.Tech or MCA preferred.
Proficiency in Python, PyTorch, Hugging Face Transformers, LangChain or similar orchestration frameworks, and cloud platforms (Azure/AWS/GCP).
Experience with REST APIs (FastAPI, Flask), ML pipeline tools (MLflow, Weights & Biases), and CI/CD for ML (e.g., Azure ML, SageMaker pipelines).
Experienced in fine-tuning and customizing foundation models with domain-specific datasets and prompt engineering.
Skilled in productionizing AI models on cloud AI platforms with collaboration across data engineering and MLOps teams.
Familiar with advanced GenAI tooling such as RAG, Vector DBs (FAISS, Pinecone), and deployment technologies like Docker and Kubernetes is a plus.