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Job Description: Equifax is seeking a visionary AI engineer to join our technology transformation initiative. In this role, you will lead a talented team in architecting and deploying cutting-edge, cloud-native solutions for a large enterprise. You will be at the forefront of modern development, employing vibe coding concepts with AI-powered coding assistants like GitHub 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. You're not just a coder; you're an architect, a mentor, and a key driver of the team's technical vision. If you are passionate about solving complex problems and mentoring a high-performing team, we want to hear from you. What youll do As our Senior AI Engineer, you will be the driving force behind our technical vision and execution. Your core responsibilities include: Implement Sophisticated AI Agents: Design, build, and deploy complex AI agents using LangChain and LangGraph. 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. Youll 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. Champion MLOps for Agentic Systems: Establish and lead best practices for the reliability, versioning, monitoring, and observability of our 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 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 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 Lead with a data/metrics driven mindset with a extreme focus towards optimizing and creating efficient solutions 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 present complex information in a concise format that is audience appropriate What You'll Bring (Experience & Technical Stack) Bachelor's degree or equivalent experience 5+ years in software engineering, with a strong track record of technical leadership and shipping complex, scalable systems. 1+ 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 LangChain, LangGraph, 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/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 .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.