- Company Name
- KPMG UK
- Job Title
- Lead AI Engineer
- Job Description
-
Job title: Lead AI Engineer
Role Summary:
Senior technical leader who designs, builds, and deploys scalable AI solutions that enhance audit workflows. Leads a squad of engineers and data scientists, mentors junior staff, and drives MLOps best practices across the organization.
Expectations:
- Deliver end‑to‑end AI systems from proof‑of‑concept to production.
- Maintain high code quality, performance, and security standards.
- Keep abreast of emerging AI and cloud technologies.
- Engage with clients and audit professionals to align solutions with business needs.
Key Responsibilities:
- Architect and develop production‑grade AI pipelines, APIs, and data integration workflows on Azure and Databricks.
- Own the deployment and operational monitoring of models using MLOps practices (CI/CD, model versioning, monitoring, and rollback).
- Mentor and coach junior engineers, promoting clean coding, testing, and collaboration.
- Collaborate with product owners, data scientists, QA, and platform engineers to define requirements, timelines, and quality criteria.
- Contribute to reusable patterns, coding standards, and knowledge‑sharing initiatives within the audit technology function.
- Participate in client engagements to gather requirements and demonstrate solution value.
Required Skills:
- Extensive backend development experience; senior engineer level.
- Deep proficiency in Python, including asynchronous programming (asyncio, asyncio‑based frameworks).
- Strong background in AI/ML fundamentals, model training, evaluation, and deployment.
- Practical experience with generative AI models (LLMs), Azure AI services, and Databricks.
- MLOps tools: Docker, Kubernetes, Terraform, GitOps, CI/CD pipelines, model monitoring.
- Cloud‑native architecture: Azure Functions, Azure Synapse, Azure ML, or equivalent.
- Excellent problem‑solving, communication, and teamwork skills.
Required Education & Certifications:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
- Optional certifications in Azure AI/ML, Databricks, or MLOps (e.g., Microsoft Certified: Azure AI Engineer Associate, Databricks Certified Data Engineer) are advantageous.