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 Sky Deck, 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
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 For Must-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, Hugging Face, Open AI / LLM APIs, Langgraph
Backend: Node.js (Express), Python (Fast API / Flask)
Frontend: Vue.js (or strong JS fundamentals + willingness to ramp fast)
Databases: Mongo DB, 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 Linked In
Git Hub / portfolio/project you’re proud of
(Optional) A short note on what kind of systems you like building
