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 large‑language‑model (LLM) solutions that empower 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 Retrieval‑Augmented Generation (RAG) pipelines, agentic workflows, and prompt‑engineering frameworks into enterprise services.
• Architect 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: 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 experts to translate requirements into maintainable AI solutions.
• Lead technical design discussions, contribute to architectural decisions, and conduct code reviews.
• Produce comprehensive documentation covering AI architecture, data sources, model design, API specs, and deployment processes.
• Champion responsible AI principles, adhering to data privacy, security, and regulatory standards.

Required profile

• Proven experience designing and deploying production‑grade AI/ML solutions.
• Strong knowledge of LLMs, Generative AI, and prompt engineering.
• Hands‑on experience with cloud platforms Azure, AWS, or GCP.
• Demonstrated expertise in MLOps, CI/CD, and model lifecycle management.
• Ability to work collaboratively with cross‑functional and tax‑domain teams.

Required skills

• Large Language Models (LLM)
• Generative AI
• Retrieval‑Augmented Generation (RAG) pipelines
• Agentic workflows
• Prompt engineering
• Azure, AWS, GCP
• Supervised and unsupervised learning
• Natural Language Processing (NLP)
• Fine‑tuning of large language models
• MLOps practices
• CI/CD pipelines