AI Agent Engineer at Cialfo

, delhi, India -

Full Time

Start Date

Immediate

Expiry Date

29 Aug, 26

Salary

0.0

Posted On

31 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Agents, LLMs, Python, Vector Stores, Prompt Management, Evaluation Frameworks, Observability Tooling, System Architecture, Orchestration Patterns, Model Selection, API Integration, Product Engineering

Industry

Software Development

Description

About Manifest Global Manifest Global is building the infrastructure for global human capital mobility - connecting students, schools, universities, and employers across 50+ countries. Our portfolio spans Cialfo (AI-powered college counseling, 2,000+ schools), BridgeU (university guidance for international schools globally), Kaaiser (trusted study abroad counseling across India and Southeast Asia), and Explore (AI-powered university outreach, 1,000+ university partners). Together, we move talent across borders at scale. $80M raised. Still early. What This Role Is AI agents — systems that reason, plan, take action across tools, and complete real work end-to-end — have moved from research demos to production infrastructure in the last eighteen months. The companies that build them well will define the next decade of what software is and what people do. Manifest Global sits at one of the most operationally complex intersections in the world. Every day, our platforms move students between continents, counselors between conversations, applications between deadlines, universities between recruiting cycles, and agents between thousands of families making generational decisions. The work is high-stakes, high-volume, and full of judgment calls — exactly the kind of work that breaks under brittle automation and exactly the kind of work AI agents, built properly, can transform. That is the bet behind this role. Not chatbots. Not wrappers. Agents that actually do the work — drafting university shortlists with reasoning a counselor would defend, surfacing the right student profile to the right admissions officer, catching the document that's about to make an application fail before a human sees it. Agents that hold context across long workflows. Agents that know when to escalate and when to keep going. Agents that get measurably better as more of the network uses them. You will build those agents across the Manifest portfolio, in production, at a scale that matters. What Makes This Role Different Most companies hiring AI agent builders right now have one of two problems. They have the data but no real product surface to deploy into, so the agents sit in demos waiting for distribution. Or they have the distribution but the underlying data is too fragmented to support agents that work in production. Manifest has both. Cialfo's 2,000+ schools, BridgeU's international school footprint, Kaaiser's three decades of placement data, Explore's 1,000+ universities — that is the substrate. Saige, already live inside Cialfo, is proof that AI in this domain works when it is built well. Your job is to extend that surface across the portfolio. This is not a research role. The agents you build will be used by real counselors, students, admissions officers, and partners within weeks of being shipped, not quarters. The feedback loop is tight. The accountability is direct. If an agent you built makes a bad shortlist, a counselor will tell you the same week. That is the speed at which good AI products get built right now, and it is the speed this role operates at. The broader EdTech market is contracting. Manifest is growing. AI is the lever — and the people who build the agents will define what the next version of this company looks like. What You Own The agent platform Design and build production AI agents across Manifest's brands, starting with the highest-leverage workflows in counseling, admissions outreach, and operational triage Own the architecture decisions: model selection, tool use, memory, evaluation, guardrails, and the orchestration layer that holds it together Build the abstractions that turn one good agent into a platform Evaluation and reliability Define what "working" means for each agent in measurable terms, and build the evaluation infrastructure that proves it Own latency, cost, and accuracy as engineering disciplines, not afterthoughts Build the safety layer: when an agent should stop, when it should escalate, when it should ask Integration into product Work directly with product and engineering teams across Cialfo, BridgeU, Kaaiser, and Explore to ship agents into the surfaces where users already are Translate fuzzy user problems into agent specifications that can actually be built Close the loop between user feedback and model behaviour faster than anyone in this industry currently does Technical direction Set the standard for how AI agents are built at Manifest — tooling, frameworks, model providers, evaluation harness, deployment patterns Stay close enough to the research frontier to know what is becoming possible six months out, and ship accordingly What Success Looks Like The markers below reflect where Manifest's AI agent function is today. We'll calibrate the specifics once you're in the seat. These are directional, not fixed. In the first weeks, you have a clear point of view on which workflows across the portfolio are the highest-leverage places to deploy agents — and you can defend that view to the counselors, product leaders, and engineers who will use what you build. You have started building, not just planning. Something is in the hands of a real user by the end of month three. By mid-period, at least one agent you have built is in production, used daily, and measurably better than the manual workflow it replaced. The evaluation infrastructure exists and is trusted by the people whose work depends on it. Other engineers across the group know how to build agents the Manifest way because you have made that pattern legible. Longer term, the agent layer is infrastructure, not a project. New product surfaces ship with AI agents as a default, not an exception. The work you built outlasts the specific models you started with, because the architecture you chose absorbed model upgrades without rewriting from scratch. The specifics will be calibrated once you're in the role. The direction won't change. What You Bring You have spent the better part of your career building software that runs in production — and somewhere in the last two years, you became one of the people other engineers go to when they have a hard question about LLMs, agents, or how to actually make this stuff work outside a notebook. You have shipped something agentic. Not a prototype. Something users use. You know the current frontier well enough to be opinionated about it. You have working views on which model providers to use for what, which orchestration patterns hold up under real load, where the abstractions in popular agent frameworks leak, and how to evaluate something that doesn't have a single correct answer. You write production-quality Python. You are comfortable with the surrounding stack: vector stores, prompt management, evaluation frameworks, observability tooling. You think in systems, not in prompts. When you see a workflow, you instinctively map where reasoning happens, where state lives, where tools get called, where humans need to stay in the loop. You build for the model that ships in six months, not the one that exists today. You are self-directed in the way this kind of role requires. There is no finished playbook for what you are building. The questions you will face — which agent to build next, how much to invest in evaluation infrastructure, when to use a smaller model and when to use a frontier one — will not have obvious answers. You make calls, you ship, you learn, you adjust. Somewhere underneath the technical depth, you care about what the agents you build actually do in the world. The fact that a student in Delhi or Manila or Lagos is going to make a better university decision because of code you wrote — that matters to you. Most importantly, you read the description of what Manifest is building in AI and your first reaction was the agents I want to build are exactly the ones this company needs built. That's the person this role is for. Why Manifest Manifest Global is building the infrastructure for global human capital mobility — connecting students, schools, universities, and employers across 50+ countries. Our portfolio spans Cialfo (AI-powered college counseling, 2,000+ schools), BridgeU (university guidance for international schools globally), Kaaiser (trusted study abroad counseling since 1997 across India and Southeast Asia), and Explore (AI-powered university outreach, 1,000+ university partners). Together, we move talent across borders at scale. $700B flows annually in remittances from migrant workers. 85M workers will be missing from developed economies by 2030. We're building the operating system which changes that. $80M raised. Still early. For this role specifically, the AI agents you build are not adjacent to the mission — they are the mission's primary lever. Counselors cannot serve every student who needs guidance. Universities cannot manually reach every qualified applicant. Operations teams cannot manually track every application across every destination. The only way the infrastructure we are building reaches the scale the problem actually demands is through AI agents that hold their own in real work. That is what this seat is for. Manifest Global is building the infrastructure for global human capital mobility, operating across 50+ countries.

Responsibilities

Design and build production-ready AI agents across the Manifest portfolio to automate complex workflows in counseling and admissions. Own the entire agent lifecycle, including architecture, evaluation infrastructure, and integration into existing product surfaces.