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About FirstIgnite FirstIgnite makes software for university tech transfer offices. Those are the people who take research coming out of a university lab and get it patented, licensed, or spun out into a company. The role We're hiring a Senior AI Agent Engineer. You'll build the agents in our product, and you'll build the evals that tell us whether each change made them better or worse. The work is document-heavy rather than chat. The agents run multi-step, call tools, read long and inconsistently formatted source material, check it against existing records, and produce output that a person reviews before anything happens with it. Accuracy matters more here than speed or novelty. Most of the engineering effort goes into precision, traceability, and getting the agent to hand off to a human at the right moment. You'll report to the Head of Engineering and work with product and the full-stack team. If you've shipped agents before, you've probably had the experience of changing a prompt and having no idea whether you improved anything. That problem is most of this job. What you'll do • Design and ship long-running, multi-step, tool-using agents on various AI SDKs and tooling, included but not limited to the OpenAI Agents SDK, the Anthropic Agent SDK, the Vercel AI SDK, LangGraph, MCP, and Temporal Cloud. • Wrap our APIs and our partners' APIs as tools an agent can call over MCP. Some of those systems are old, single-tenant, and outside our control, so a fair amount of the work is translation. • Get structured data out of long documents and match it against records that already exist. Expect entity resolution and fuzzy matching, and expect much of it to run in batch. • Stand up eval suites using various evaluation frameworks and tooling, included but not limited to Promptfoo, Braintrust, LangSmith, DeepEval, LLM-as-judge methods, and custom harnesses. Measure tool-use correctness, trajectory quality, and whether the agent finished the task. • Every agent here produces a draft that a person signs off on. Build the citations and confidence signals that make that review fast, and give the agent a clear way to escalate. • Sit with product and domain experts and turn vague quality goals into something measurable. Sometimes the only dataset available for that is tiny, or confidential, or both. • Instrument production traffic, turn real customer interactions into golden datasets, and run them as regression tests. • Compare models against each other (OpenAI, Anthropic, open-weight), along with prompt strategies and agent designs, and know what each option costs in latency and quality. • Bootstrap quality signal for features that have no production traffic yet. That usually means generating synthetic documents and test cases, including the ugly edge cases real customers will eventually send us, and knowing where synthetic data stops being a good proxy. • Write the templates, docs, and tooling the rest of the team needs to run evals without coming to you. Required Qualifications: • 3+ years of engineering experience, including hands-on work on LLM or agent systems that real users touched. • You've evaluated agents, not only models, and you know why single-turn accuracy says little about a multi-step run. • You've integrated against APIs you don't own, including old ones with bad documentation, and turned them into something an agent can call reliably. • You're comfortable with document pipelines: pulling data out, normalizing it, and checking it against a structured source of truth. • You've used at least one LLM evaluation framework, in-house tooling included. • You know how LLM-as-judge methods break down (position bias, verbosity bias, judge drift) and what to do about it. • You can tell a real regression from noise, and design an experiment that answers the question being asked instead of a nearby one. • You can read a customer call transcript, work out which failures matter, and ship a fix and an eval for them. • You write clearly. Engineers won't act on eval results they don't read or don't trust. • You're based somewhere between New York time (ET) and Western European time. Italy is the furthest east we can go. You might currently be titled AI Agent Engineer · Agentic AI Engineer · Senior AI Engineer · AI Systems Engineer · Lead AI Developer · AI Solutions Architect · AI Integration Specialist · Senior Data Scientist, AI Titles are all over the place in this space. If the work above matches what you already do, apply. We'll go on what you've shipped. Preferred Qualifications: • You've evaluated retrieval systems: RAG, hybrid search, reranking. • You've worked with agent orchestration frameworks like Temporal, LangGraph, or the OpenAI Agents SDK, and you know how long-running tool use goes wrong. • You have a background in information retrieval or search relevance. • You've worked somewhere an agent's output carried financial or compliance consequences. • You've built internal tooling that non-engineers used on their own to label and review model output. This is a fully remote, full-time permanent position available to candidates located within the New York (ET) through Western Europe time zones, with flexible working hours to support collaboration across regions.
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.