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Strong PwC brand, mid-level experience band, metro location, and broad demand for GenAI skills increase competition.
GenAI engineering skills are transferable across industries but consulting integration and enterprise deployment raise moderate domain specificity.
Mandatory 4–7 years, GEN AI and Python/PySpark plus cloud and tooling requirements enforce strict shortlisting.
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Design and implement Generative AI applications such as chatbots, document summarization tools, code assistants, or knowledge search solutions.
Develop and deploy scalable AI pipelines on cloud platforms (primarily AWS, Azure acceptable) integrating LLMs, vector databases, and retrieval-augmented generation workflows.
Ensure integration of AI solutions into enterprise applications with attention to data security, access control, cost optimization, and API/microservices architecture.
4 to 7 years of work experience in Generative AI and Data & Analytics role.
Strong proficiency in Python/PySpark and experience with GenAI concepts including LLMs, prompt engineering, fine-tuning, and model evaluation.
Experience with cloud platforms for AI deployment, especially AWS services such as Bedrock, SageMaker, Lambda, Step Functions, API Gateway, and CloudWatch.
Educational qualifications: MBA, MCA, or BTech degree.
Experienced at building and operationalizing AI applications using modern frameworks like LangChain, Transformers, PyTorch, TensorFlow, and LLM APIs (OpenAI, Anthropic, Claude, Llama).
Skilled in managing end-to-end AI pipelines including embedding generation, vector databases (OpenSearch, Pinecone, FAISS), and RAG pipelines in an enterprise setting.
Capable of collaborating in Agile environments, employing MLOps/DevOps, containerization (Docker, Kubernetes) and integrating AI solutions with business intelligence or automation workflows.