AI-Native Platform Engineering Lead

About Newton Russell

Newton Russell builds the future of risk and compliance for financial services. Our platform Brontë powers critical risk and compliance workflows across banking insurance and broader financial services. We move fast. We build in person. We back our people to do their best work.

Role Overview

The AI-Native Platform Engineering Lead owns the engineering charter for the AI-Native Businesses & Platforms unit. This role is responsible for building modernizing and scaling AI-native platforms with the rigor velocity and reliability of world-class product companies and for transforming how the unit builds software so that velocity and quality become structural advantages rather than heroics.

Reporting into the CEO AI-Native Business & Platforms Unit this role owns platform engineering end-to-end from the development lifecycle through to production-grade multi-tenant platforms operating with a long-term product engineering mindset while remaining deeply grounded in real client environments. You will lead all Capability Leads & FDEs shape a differentiated engineering culture and ensure that our platforms are built to scale evolve and endure beyond individual engagements.

Key Responsibilities

• Own the engineering strategy architecture (cloud-native modular API-first multi-tenant AI-ready) and execution across all AI-native platforms and incubated businesses.

• Establish the units AI-native software development lifecycle AI embedded across requirements code generation review testing documentation and release and the golden paths that make the AI-accelerated workflow the default for every engineer.

• Roll out and govern AI developer tooling (agentic coding AI code review test generation) including adoption guardrails security posture and IP/licensing; re-architect CI/CD and quality gates for a world where a large share of code is AI-authored.

• Build and lead high-performing global engineering teams responsible for core platform development AI enablement modernization reliability and security.

• Establish engineering standards for AI-native architectures agentic systems data platforms cloud-native design and compliance.

• Partner closely with Product Management and Forward-Deployed Engineering to translate product intent into scalable production-grade platforms.

• Drive long-term technical investments balancing innovation platform velocity and operational stability.

• Modernize existing platforms to AI-native (SLMs agentic knowledge graphs).

• Optimize cost per inference / transaction as usage grows a critical capability for consumption-based pricing.

• Enable FDE-friendly extensibility (configuration > custom code): build sandbox environments plug-in architectures and tooling so field teams can extend platforms without touching core code.

• Develop senior engineering leaders and foster a culture of ownership accountability and continuous learning.

• Attract and retain top AI and platform engineers; encourage IP creation through patenting programs.

Capabilities Required

• Strong systems-thinking and the ability to engineer platforms that operate in complex regulated real-world enterprise environments.

• Deep experience building and scaling enterprise AI-native platforms used by multiple customers.

• Proven ability to lead an engineering organization through a step-change in how it builds a tooling and workflow transformation not just tech selection.

• Credibility with senior engineers and executives alike; ability to lead with clarity empathy and decisiveness.

• Comfort operating in ambiguity and evolving problem spaces.

Prerequisites

• 8 years of experience in product or platform engineering leadership.

• Background in product-first / AI-native technology companies (AI platforms/software enterprise software data).

• Demonstrated track record of both building durable scalable platforms and modernizing an engineering organizations development lifecycle.

• Strong technical foundation in Computer Science or Engineering.

Decision Rights & Interfaces

• Final authority on tech stack architecture coding standards and the engineering toolchain / SDLC.

• Sets policy on AI usage in development code provenance and quality gates.

• Joint planning with Product on feasibility and timelines.

• Works with the Security/CTO organization while retaining product-company speed.

• Strong interlock with FDEs on implementing supporting and upgrading platforms; feedback loop for field-discovered platform gaps and required features.

Success Metrics (1224 months)

• AI-native SDLC live and adopted across the unit: meaningful reduction in lead time (idea production) and cycle time (PR merge) with change-failure and defect-escape rates held flat or improved despite higher velocity.

• Platforms handle 35 usage growth without instability (99.9% availability).

• Reduced deployment effort per new client (measured in engineering-weeks).

• Predictable release cadence (quarterly major updates delivered on schedule).

• Improved cost efficiency as consumption scales (declining cost per API call / per model inference).

• Attract and retain specialized product/platform engineering talent.

Why Newton Russell

• A new domain to master. If youve built credibility from scratch in unfamiliar territory before a new industry a new technical domain of financial services risk is the next one worth learning. Were not looking for someone whos only ever done this.

• Real ownership. Youre not joining to execute someone elses architecture youre co-authoring it for AI in a category most vendors havent figured out.

• Global ambition startup pace. We move fast but were built for regulated clients across APAC Europe and North America from day one not retrofitting compliance later.

• Meaningful problem. Youre building the platform behind AI that acts on real risk in real institutions not another internal chatbot.

• Flexibility: Remote-first with overlap hours for collaboration across our regions.

How to Apply

Please submit your CV to and quote AI-Native Platform Engineering Lead in the subject line.