AI Engineer, Manager, Digital Innovation

Overview

In this AI Engineer role within Deloitte’s Tax Digital Innovation team, you apply data science and AI to deliver analytics-driven solutions for tax and legal clients. You will collaborate with a multi-disciplinary team to design, build, and productionise AI/ML capabilities that streamline tasks and support decision making. You’ll work across data, engineering, and cloud domains to deploy scalable solutions and bring ideas from concept to production. This is a hands-on, impact-focused opportunity in a fast-growing practice with hybrid work in London.

Pay / Benefits

• hybrid working London
• return-to-work program
• flexible working arrangements
• wellbeing commitment
• world-class development
• career growth opportunities

Responsibilities

• Collaborate with T&L SMEs to understand client challenges and capture requirements
• Design, develop, document, and deliver analytics, automation, and AI solutions
• Clean, verify, and source data as needed for analysis
• Analyze complex datasets and present results to technical and non-technical stakeholders
• Support in-house and cloud-based code maintenance; contribute to end-to-end data pipelines
• Ideate novel AI/automation solutions aligned with project pipelines
• Move data science solutions from POC to full production systems
• Create, monitor, and troubleshoot APIs and APIs-led integrations
• Collaborate effectively with business teams and the data science team

Key requirements

• Full-stack software or ML engineering experience with strong Python practices (CI/CD, testing, asynchronous execution, PEP8)
• Experience deploying production-scale AI/ML solutions (APIs, microservices, cloud deployment, pipelines)
• Experience with LLM-based applications (NLP, embeddings, semantic search, RAG, fine-tuning)
• Proficiency with ML tools and ecosystem (PyTorch, MLFlow, FastAPI, SQLAlchemy)
• Experience data wrangling from structured, unstructured, and web sources (Selenium/PlayWright, Pandas)
• Azure experience including Fabric, Data Factory, ML Studio, Synapse, Cosmos
• Evidence of leading end-to-end data client projects from POC to production/cloud
• GenAI/ML model deployment in production; ML Ops and LLM Ops familiarity

• Team player
• Proactive ideation and curiosity
• Attention to detail
• Python development (CI/CD, testing, multi-threading/async)
• Production AI/ML deployment (APIs, microservices, cloud)
• LLM/NLP and embeddings, semantic search, RAG

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