Principal Engineer- Generative AI & Developer Experience

Equifax is seeking an AI-SDLC Engineer to lead the structural and philosophical transformation of our engineering processes. This role is designed for a technical authority capable of transitioning the organization from traditional development patterns to an AI-Driven Development Life Cycle (AI-DLC).
 

As a high-level technical specialist, you will act as a Force Multiplier. Your primary mission is to build the underlying intelligence layers—proprietary Small Language Models (SLMs) and agentic scaffolds—that enable our engineering teams to generate, audit, and maintain code with unprecedented velocity. You will architect the automated systems that produce code, allowing the broader developer community to consume these outputs rather than authoring boilerplate from zero. You will navigate the technical landscape to ensure our AI-driven innovations are seamlessly integrated, secure, and aligned with enterprise standards.

• Model Engineering & Training (60-70% Focus): Lead the research, data curation, and training/fine-tuning of Small Language Models (SLMs) on internal repositories. You will focus on creating localized inference engines to solve high-impact structural challenges, such as developing self-healing agents for web-scraping selectors.

• Architecting the AI-DLC Ecosystem: Design the technical framework through which your proprietary models are consumed by the engineering organization. You will engineer the workflows and automation patterns that allow developers to utilize your custom-trained intelligence—via interfaces like GitHub Copilot—to eliminate manual, repetitive coding tasks.

• Strategic Mentorship & Implementation (30% Focus): Serve as the author of the "AI-DLC Playbook". You will provide the technical blueprint and mentoring that allows developers across all seniority levels to effectively leverage AI-driven models within their daily production workflows.

• Autonomous Pipeline Intelligence: Design and implement "LLM-as-a-Judge" frameworks within GitHub Actions. These systems must be capable of pre-executing code, auditing logic, and identifying semantic security vulnerabilities automatically before human intervention.

• Technical Diplomacy & Standards: Act as the lead technical liaison with global architects and senior stakeholders. You will navigate corporate approval processes to negotiate the requirements and security benchmarks necessary to deploy non-deterministic AI tools at an enterprise scale.

• Scalable Quality Automation: Architect the systems that automate the generation of unit tests and edge-case simulations, targeting a default 80%+ code coverage through AI-driven validation.

• A Bachelor’s or Master’s degree in Computer Science, Software Engineering, AI/ML, or a related technical field.

• Total Professional Experience: 7+ years of total experience in software engineering, demonstrating a comprehensive and authoritative understanding of the full Software Development Life Cycle, with the ability to apply AI-driven patterns across diverse technical stacks to ensure the integrity of the entire engineering pipeline.

  • This must include 5+ years of professional experience in backend development and system architecture with a focus on Python.

  • This must include 2+ years of hands-on experience in AI modeling, specifically training, fine-tuning, and optimizing SLMs/LLMs (weights, tokenization, LoRA, QLoRA).

• Cloud Orchestration for AI: 2+ years of experience in deploying and scaling AI/modeling workflows in cloud environments. You should be proficient in managing model registries and orchestrating inference serving using modern cloud-native solutions (GCP preferred, but AWS/Azure experience is highly valued).

• Proficiency level of B2 or higher is required for global collaboration.

• Web Scraping & Automation Expertise: Specialized experience with web-scraping optimization, anti-bot circumvention, and the development of self-healing agents for automated selector maintenance.

• DevOps & CI/CD Mastery: Proven experience building complex automation pipelines using GitHub Actions, with a focus on integrating automated gates and AI-driven security checks.

• AI Observability: Experience with platforms like Langfuse, Opik, or Arize to monitor and refine the accuracy of "Judge" models against human baselines.

• Professional proficiency in Java, enabling the eventual expansion of AI-DLC patterns to Java-based repositories.

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