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

About the role

Deloitte Tax Services India is seeking an experienced AI Engineer – Consultant to join its USI Tax Technology team. You will design, build, and deploy enterprise‑grade AI and large‑language‑model (LLM) solutions that directly 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 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.
• 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 uphold engineering best practices.
• Produce comprehensive documentation covering AI architecture, data sources, model design, API specifications, and deployment processes.
• Champion responsible AI principles, ensuring compliance with data privacy, security, and regulatory standards.

Required profile

• Proven experience as an AI Engineer or similar role, with end‑to‑end ownership of AI solutions.
• Ability to work closely with tax professionals and translate business needs into technical implementations.
• Strong collaborative mindset and experience leading technical discussions.
• Commitment to responsible AI and adherence to data governance policies.

Required skills

• Azure, AWS, GCP cloud platforms
• Machine Learning, Natural Language Processing (NLP)
• Large Language Models (LLM) and Generative AI
• Retrieval‑Augmented Generation (RAG) pipelines
• Prompt Engineering frameworks
• MLOps practices (model versioning, CI/CD, monitoring, bias detection, data drift management)