AI Engineer

About the role

The AI Engineer will lead the technology transformation initiative at Equifax, focusing on architecting and deploying cloud-native solutions for a large enterprise.

Responsibilities

• Implement Sophisticated AI Agents: Design, build, and deploy complex AI agents using LangChain and LangGraph.
• Master Prompt & Context Engineering: Design, test, and refine complex prompts and contextual data frameworks to ensure AI agents perform with maximum accuracy, efficiency, and reliability.
• Lead AI Research & Innovation: Stay at the bleeding edge of AI, 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 AI systems in a scalable, reliable production environment on Google Cloud Platform.
• Champion MLOps for Agentic Systems: Establish and lead best practices for the reliability, versioning, monitoring, and observability of AI agents, using tools like Langfuse to ensure production-grade performance.
• Collaborate to Deliver Impact: Partner closely with product leaders, data scientists, and other engineers to translate business needs into technical reality, ensuring 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.
• Build, manage, and mentor a cross-functional team of software, quality, and reliability engineers, fostering a culture of technical excellence and continuous improvement.
• Define and report on key engineering metrics (SLA, SLO, SLI) and ensure compliance with security, quality, and financial operations (DevSecOps, FinOps) best practices.
• Collaborate with product managers, architects, SREs and business partners to define technical strategy, create software roadmaps, and make key architectural and design decisions.
• 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.
• Drive up-to-date technical documentation including support, end user documentation and run books.
• Create and deliver technical presentations to internal and external technical and non-technical stakeholders, communicating with clarity and precision, and presenting complex information in a concise format that is audience appropriate.

Requirements

• Bachelor's degree or equivalent experience
• 7+ years in software engineering, with a strong track record of technical leadership and shipping complex, scalable systems.
• Experience in a dedicated AI/ML role, with hands-on experience in model integration, MLOps, and applying AI to solve business problems.
• Direct experience architecting and building solutions with LangChain, LangGraph, or similar agentic AI frameworks.
• 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/TypeScript 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 least one major cloud provider (AWS, Google Cloud, or Azure).
• Containerization: Mastery of Docker for containerizing applications and Kubernetes for orchestration.
• Infrastructure as Code (IaC): Proficiency with tools like Terraform or CloudFormation to manage infrastructure programmatically.
• CI/CD Tools: Experience with CI/CD tools such as Github Actions, Argo CD, Jenkins
• Database Knowledge: Strong experience with both SQL (e.g., Spanned DB, Alloy DB, PostgreSQL, MySQL) and NoSQL (e.g., MongoDB, DynamoDB and Firestore) databases.

Qualifications

• Strong expertise in Generative AI (GenAI), including hands-on experience with models like Gemini, ChatGPT, Claude, or Llama.
• Adept at leveraging modern development tools, including AI-powered code assistants (like GitHub Copilot), to accelerate the development lifecycle and rapidly ship high-quality features.
• Experience creating and deploying AI agents to production environments.
• Passionate about the potential of AI, but grounded in the practical realities of building and shipping reliable, production-ready software.
• Passionate about applying cutting-edge technology to solve meaningful, real-world problems at a massive scale.