Job Title

About Infis AI Infis 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 layer Detect risks early (financial, geopolitical, operational, compliance, tariff) Replace manual surveys and spreadsheets with real-time, explainable AI Automate mitigation workflows instead of just generating reports We’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 Values Honesty We say what we mean. Clear feedback > polite ambiguity. Customer Obsession We build with users, not in isolation. If it doesn’t reduce customer anxiety, it’s not done. Clear, Constant Communication We move fast across time zones. Clarity is non-negotiable. The Role We’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 On Build 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 features Improve model quality, prompts, retrieval logic, and feedback loops Make architectural decisions that scale (multi-tenancy, async workflows, data isolation) Ship fast, iterate often, and learn continuously How We Work We deploy frequently Requirements change often We value momentum over perfection We expect engineers to own problems , not just tickets Failure is acceptable; stagnation isn’t What We’re Looking ForMust-Haves 3–5 years of experience building production software Strong fundamentals in software engineering and data structures Experience building full-stack applications Hands-on experience with AI/ML systems (don’t apply if this doesn’t excite you) Comfort working with messy, real-world datasets Ownership mindset: you care about reliability, UX, and outcomes Tech We Use (or Expect You to Learn Quickly) Machine Learning / AI: Pandas, scikit-learn, Transformers, HuggingFace, OpenAI / LLM APIs, Langgraph Backend: 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 Points Experience deploying ML models. Prompt engineering or LLM-based workflows Exposure to supply chain, manufacturing, ERP, or process data Startup or 0→1 product experience Comfort discussing tradeoffs, not just implementations What This Role Is Not Not a “ticket-only” engineering role Not a research-only ML position Not a slow-moving, heavily layered org This role is for builders who want real responsibility early . What We Offer Competitive compensation + early equity Real ownership and autonomy Direct exposure to enterprise customers Fast growth in AI, systems design, and product thinking A small, opinionated team that values impact over hierarchy Ready to Build? Send us: Your resume or LinkedIn GitHub / portfolio/project you’re proud of (Optional) A short note on what kind of systems you like building