🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
About Level AI Level AI is on a mission to turn every customer interaction into a strategic advantage. Our AI-native platform helps enterprises transform contact centers from cost centers into engines of customer intelligence, operational efficiency, and business growth. By combining advanced AI with deep domain understanding of customer experience, Level AI empowers teams to unlock actionable insights, automate workflows, and deliver more consistent, higher-quality support across the customer journey. Headquartered in Mountain View, California, Level AI is a Series C company backed by leading investors including Battery Ventures and ENIAC. Our platform leverages Large Language Models and Custom Small Language Models (SLMs) to power AI Agents across the entire CX journeycustomer-facing agents, agent-assist, and backend automationalong with deep conversation analytics for QA, coaching, and insights. About the role As aSenior Engineer AI Agents, you will play a critical role in building the scalable backend systems that power Level AI's next-generation AI Agents. These systems operate in real-time, high-volume enterprise environments and are central to delivering intelligent, production-grade AI experiences. You will work at the intersection of distributed systems, cloud infrastructure, and AI-powered applicationsbringing agentic AI capabilities into production at scale. What you'll get to do at Level AI (and more as we grow together): Design and buildscalable backend systems powering AI Agentsthat operate in real-time enterprise environments Developagent orchestration frameworks(multi-step reasoning, tool usage, decisioning workflows) Build systems foragent memory, context management, and state persistenceacross interactions Architectlow-latency inference pipelinesintegrating LLMs, SLMs, and external tools/services Implementevaluation (evals) frameworksto measure agent performance, accuracy, and reliability Enablecontinuous improvement loops(feedback retraining deployment) for AI agents in production Design and manageevent-driven, asynchronous workflowsfor complex agent tasks Optimize systems forhigh throughput, low latency, and cost-efficient inference at scale Build and maintainrobust APIs and service layers(REST / gRPC) for agent capabilities Partner closely withApplied AI / ML teamsto productionize models and agent behaviours. Collaborate with Product and Solutions teams totranslate real customer workflows into agentic systems Drive best practices inobservability, monitoring, safety, and guardrails for AI systems Contribute toarchitecture decisions for scaling multi-tenant, enterprise-grade AI platforms We'll love to explore more about you if you have: 5+ years of experience in backend engineering, distributed systems, or platform engineering Strong experience buildinghigh-scale, production-grade backend systems Experience designing systems forreal-time processing, streaming, or event-driven architectures Strong understanding ofAPI design (REST, gRPC)and microservices architectures Experience withdatabases (SQL + NoSQL)and data modeling for high-scale systems Hands-on experience withDocker, Kubernetes, and cloud platforms (AWS/GCP/Azure) Strong fundamentals insystem design, concurrency, and performance optimizationStrong Plus (Agent / AI focus): Experience working withLLMs, conversational AI, or AI-powered products in production Familiarity withagent frameworks, tool calling, or multi-step reasoning systems Experience building or integratingRAG pipelines, vector databases, or retrieval systems Exposure toevaluation systems (offline/online evals, A/B testing for AI systems) Understanding ofprompting strategies, context windows, and model behavior optimization Experience withreal-time decisioning systems or workflow orchestration engines Perks & Advantages Competitive compensation with performance-based upside Flexible vacation policy Health insurance coverage Work with a globally distributed, high-impact team Prospect to build cutting-edge AI products at scale Regular team offsites and in-person collaboration To learn more visit :/ Funding : LinkedIn :/ Our AI platform : We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. .
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.