Senior AI Engineer
Listed on 2025-12-01
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Software Development
AI Engineer, Machine Learning/ ML Engineer
Equifax is where you can power your possible. If you want to achieve your true potential, chart new paths, develop new skills, collaborate with bright minds, and make a meaningful impact, we want to hear from you.
Equifax is seeking a visionary AI engineer to lead the development and deployment of cutting‑edge Artificial Intelligence solutions. In this role, you will be developing and deploying cloud‑native AI solutions for a large enterprise. You will be at the forefront of modern development, employing vibe coding concepts with AI-powered coding assistants like Git Hub Copilot, Gemini, and accelerate innovation and build highly scalable, reliable, and performant APIs, microservices, and PaaS/SaaS platforms, including the ability to design, develop, and deploy AI agents in the google cloud platform.
This role requires a deep understanding of both front‑end and back‑end technologies, combined with mastery of cloud infrastructure, containerization, microservices architecture and the agentic AI framework. If you are passionate about solving complex problems and mentoring a high‑performing team, we want to hear from you.
- As Senior AI Engineer, your core responsibilities include:
- Implement Sophisticated AI Agents:
Design, build, and deploy complex AI agents using Lang Chain and Lang Graph. You will own the core logic that automates intricate decision‑making within the claims lifecycle. - Master Prompt & Context Engineering:
Design, test, and refine complex prompts and contextual data frameworks to ensure our AI agents perform with maximum accuracy, efficiency, and reliability. - Lead AI Research & Innovation:
Stay at the bleeding edge of AI. You’ll be responsible for identifying, prototyping, and integrating the latest foundational models, RAG techniques, and agentic frameworks to solve unique business challenges. - Build for Production Scale on GCP:
Engineer and operate our AI systems in a scalable, reliable production environment on Google Cloud Platform. Your work will directly impact millions of users. - Collaborate to Deliver Impact:
Partner closely with product leaders, data scientists, and other engineers to translate business needs into technical reality, ensuring our AI solutions are both innovative and effective. - Champion modern software development practices by actively using AI code‑assist tools (e.g., Gemini code assists, Github Copilot, Claude code) to accelerate development cycles, generate documentation, improve code quality, testing, and monitoring & observability practices.
- Define and report on key engineering metrics (SLA, SLO, SLI) and ensure compliance with security, quality, and financial operations (Dev Sec Ops , Fin Ops) best practices.
- Lead troubleshooting efforts to resolve production and customer issues, demonstrating deep technical expertise and problem‑solving skills.
- Participate and lead agile team activities, including Sprint Planning and Retrospectives, to ensure efficient and predictable delivery.
- Lead with a data/metrics driven mindset with an extreme focus towards optimizing and creating efficient solutions.
- Drive up‑to‑date technical documentation including support, end user documentation and run books.
- Bachelor's degree or equivalent experience
- 5+ years of software engineering experience
- 5+ years experience writing, debugging, and troubleshooting code.
- 2+ years in a dedicated AI/ML role, with hands‑on experience in model integration, MLOps, and applying AI to solve business problems.
- 1+ years of direct experience architecting and building solutions with Lang Chain, Lang Graph, or similar agentic AI frameworks.
- 2+ years of in‑depth experience with Google Cloud Platform (GCP), specifically its AI/ML services (Vertex AI, etc.).
- 3+ years of proven experience leveraging Kubernetes workloads.
- Proficiency in Python, JavaScript/Type Script and/or Java and working knowledge of a modern front‑end framework (Angular, React, or Vue) to collaborate effectively with UI teams.
- Hands‑on experience with LLM observability tools like Langfuse for monitoring and debugging agentic workflows.
- Cloud‑Native Proficiency:
- Cloud Platforms:
Extensive hands‑on experience with at…
- Cloud Platforms:
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