Job Title

About Infis AIInfis AI is building the AI co-pilot for supplier risk and procurement teams in manufacturing and pharma.Today, critical supplier decisions depend on fragmented systems, such as ERP, MES, emails, PDFs, spreadsheets, and reactive workflows. A single missed signal can shut down a production line, delay drug manufacturing, or trigger millions in losses.We build systems that:Aggregate structured + unstructured data into a unified supplier intelligence layerDetect risks early (financial, geopolitical, operational, compliance, tariff)Replace manual surveys and spreadsheets with real-time, explainable AIAutomate mitigation workflows instead of just generating reportsWe’re backed by Alchemist Accelerator, Berkeley SkyDeck, and NECX, and we’re working directly with enterprise manufacturing and pharma teams.If you enjoy AI + messy real-world data + systems that actually get used, you’ll like it here.Our ValuesHonestyWe say what we mean. Clear feedback > polite ambiguity.Customer ObsessionWe build with users, not in isolation. If it doesn’t reduce customer anxiety, it’s not done.Clear, Constant CommunicationWe move fast across time zones. Clarity is non-negotiable.The RoleWe’re looking for a Full Stack AI Engineer who will be part of the core engineering group at Infis AI.This is not a narrow “ML-only” or “frontend-only” role. You’ll own features end-to-end—data ingestion → AI logic → backend APIs → product UI—and work closely with the founders and customers.You should be comfortable operating in ambiguity, making technical decisions, and shipping continuously.What You’ll Work OnBuild and deploy AI / ML workflows on structured and unstructured data.Build AI Agents to handle complex manufacturing crisis management workflow.Build Voice agents to automate manual comms across the ecosystem.Design and own backend services (Node.js / Python) for data ETL processes, Risk scoring, inference, and automation workflows.Work directly with the founding team to turn customer pain points into shipped featuresImprove model quality, prompts, retrieval logic, and feedback loopsMake architectural decisions that scale (multi-tenancy, async workflows, data isolation)Ship fast, iterate often, and learn continuouslyHow We WorkWe deploy frequentlyRequirements change oftenWe value momentum over perfectionWe expect engineers to own problems, not just ticketsFailure is acceptable; stagnation isn’tWhat We’re Looking ForMust-Haves3–5 years of experience building production softwareStrong fundamentals in software engineering and data structuresExperience building full-stack applicationsHands-on experience with AI/ML systems (don’t apply if this doesn’t excite you)Comfort working with messy, real-world datasetsOwnership mindset: you care about reliability, UX, and outcomesTech We Use (or Expect You to Learn Quickly)Machine Learning / AI:Pandas, scikit-learn, Transformers, HuggingFace, OpenAI / LLM APIs, LanggraphBackend:Node.js (Express), Python (FastAPI / Flask)Frontend:Vue.js (or strong JS fundamentals + willingness to ramp fast)Databases:MongoDB, Neo4j (or similar)Infra:REST APIs, async jobs, Docker (nice-to-have)Bonus PointsExperience deploying ML models.Prompt engineering or LLM-based workflowsExposure to supply chain, manufacturing, ERP, or process dataStartup or 0→1 product experienceComfort discussing tradeoffs, not just implementationsWhat This Role Is NotNot a “ticket-only” engineering roleNot a research-only ML positionNot a slow-moving, heavily layered orgThis role is for builders who want real responsibility early.What We OfferCompetitive compensation + early equityReal ownership and autonomyDirect exposure to enterprise customersFast growth in AI, systems design, and product thinkingA small, opinionated team that values impact over hierarchyReady to Build?Send us:Your resume or LinkedInGitHub / portfolio/project you’re proud of(Optional) A short note on what kind of systems you like building