AI Software Engineer - Talent Pool

TITLE AI Software Engineer / Architect

(Developer Productivity, AI Governance & Engineering Standards)

DEPARTMENT Product Engineering

REPORTING TO: Senior Manager of Architecture

OFFICE LOCATION: New York, NY

ROLE TYPE: Hybrid

IPC is a global fintech company that puts people at the center of innovation. With a strong global footprint, we empower financial institutions and capital markets with advanced cloud-based trading communications and managed connectivity solutions.

Through our portfolio of communications and connectivity solutions, we focus on solving business challenges and adapting to regulatory changes in the fast-paced global financial markets. This enables our clients to maintain consistent market access, a strong competitive advantage, and enhanced operational efficiency.

Join a team that is dedicated to delivering groundbreaking products and making a significant impact on our clients’ success.

­­­­­Overview of the

Team Product engineering is responsible for the development of products and services focusing on digital telecommunication and API development. The team is organized into several Agile scrum teams for planning, development, and validation of Connexus Unigy among other products.

This role operates horizontally across the engineering organization, partnering with platform, application, security, and DevOps teams to define how AI-assisted software engineering is practiced at IPC.

Role Overview:

We are seeking an AI Software Engineer / Architect to lead the definition, adoption, and governance of AI-assisted development practices across IPC’s engineering organization. This role combines hands-on software engineering, architecture leadership, and organizational enablement. You will define best practices, patterns, guardrails, and workflows for the use of AI coding tools such as Claude Code and GitHub Copilot, ensuring they are used safely, consistently, and effectively across all engineering teams.

The ideal candidate is both a strong engineer and a pragmatic architect who understands how to scale new development paradigms across large teams without sacrificing code quality, security, or maintainability.

Key Responsibilities:

AI-Assisted Engineering Strategy & Standards

• Define IPC’s engineering standards, patterns, and best practices for AI-assisted software development.
• Establish approved usage models for tools such as Claude Code, GitHub Copilot, and related AI developer tooling.
• Create architectural guardrails to ensure AI-generated code aligns with IPC’s security, performance, and maintainability requirements.
• Continuously evaluate emerging AI development tools and techniques, recommending adoption where they provide measurable value.

Software Architecture & Code Quality

• Define reference architectures and coding patterns optimized for AI-assisted development.
• Ensure AI-generated code adheres to IPC’s standards for modularity, testability, observability, and long-term maintainability.
• Review and influence system designs where AI-assisted development meaningfully impacts architecture decisions.
• Partner with architecture and platform teams to ensure AI usage aligns with broader technical direction.

Developer Enablement & Organizational Adoption

• Work across engineering teams to drive consistent adoption of approved AI development workflows.
• Develop internal guidance, examples, playbooks, and documentation for effective AI-assisted coding.
• Coach engineers on how to use AI tools responsibly and productively without over-reliance or loss of engineering rigor.
• Act as a trusted advisor to engineering leadership on AI-related productivity, risk, and capability maturity.

Security, Compliance & Risk Management

• Define policies and guardrails around data handling, IP protection, and secure usage of AI coding tools.
• Ensure AI-assisted development complies with IPC’s security, regulatory, and audit requirements.
• Partner with Security and Risk teams to review and approve AI tooling configurations and usage models.
• Establish patterns for validating, testing, and verifying AI-generated code before production use.

Metrics, Feedback & Continuous Improvement

• Define metrics to measure the effectiveness of AI-assisted development (productivity, quality, defect rates, etc.).
• Gather feedback from engineering teams and continuously refine standards and guidance.
• Identify failure modes, anti-patterns, a…