AI Engineer – Consultant (Tax) – Multiple Cities, India

About the role

Deloitte Tax Services India is looking for an experienced AI Engineer – Consultant to join its USI Tax Technology team. You will design, build, and deploy enterprise‑grade AI and LLM‑powered solutions that support tax professionals and clients across India.

Key responsibilities

• Design and develop production‑grade LLM‑powered applications and Generative AI solutions for complex tax‑related challenges.
• Build and integrate AI capabilities such as Retrieval‑Augmented Generation (RAG) pipelines, agentic workflows, and prompt‑engineering frameworks into enterprise services.
• Architect and implement scalable AI integrations on Azure, AWS, and GCP, ensuring reliability, performance, and security.
• Develop, train, evaluate, and optimise machine‑learning models using structured and unstructured data, applying supervised/unsupervised learning, NLP, and fine‑tuning of large language models.
• Own MLOps practices including model versioning, CI/CD pipelines, performance monitoring, bias detection, data‑drift management, and model retraining workflows.
• Collaborate with data scientists, software engineers, business analysts, and tax professionals to translate requirements into scalable AI solutions.
• Lead technical design discussions, contribute to architectural decisions, conduct code reviews, and promote engineering best practices.
• Produce comprehensive documentation covering AI architectures, data sources, model design, API specifications, and deployment processes.

Required profile

• Experienced AI Engineer – Consultant with a strong background in AI/ML technologies.
• Ability to take end‑to‑end ownership of AI solutions from conception through production deployment.
• Strong collaboration skills to work with cross‑functional teams including tax experts.

Required skills

• Large Language Model (LLM) development and deployment
• Generative AI and Retrieval‑Augmented Generation (RAG)
• Agentic workflow design and prompt engineering
• Cloud platforms: Azure, AWS, GCP
• Machine learning techniques: supervised learning, unsupervised learning, NLP
• Fine‑tuning of large language models
• MLOps practices: model versioning, CI/CD pipelines, performance monitoring, bias detection, data‑drift management