AI Engineer

• Design and build LLM‑driven agents to support planning, optimization, and decision‑making workflows
• Develop agent orchestration patterns (tools, memory, evaluation, guardrails)
• Build and maintain RAG pipelines , structured prompting, and retrieval strategies
• Treat simulation as a first‑class design tool (using synthetic data, scenario replay, and state machines to validate logic)

• Build end‑to‑end product features spanning: Backend services (Python / TypeScript) APIs (REST / GraphQL / FastAPI) Frontend experiences (React)
• Backend services (Python / TypeScript)

• Frontend experiences (React)
• Create human‑in‑the‑loop interfaces for AI systems (review, override, explainability)
• Collaborate closely with Product and Design to ship usable, explainable AI features

• Own services in production: performance, reliability, observability, and cost
• Design systems that are secure, testable, and compliant by default
• Use infrastructure‑as‑code and modern CI/CD practices
• Champion simulation‑led design , testability, and fast feedback loops

• AI agents are shipping as core product capabilities , not experiments
• Simulation and synthetic data are actively used to design and validate features
• Product teams can move faster because AI systems are observable, testable, and trusted
• Engineering quality improves without slowing delivery

IFS.ai Logistics is building the next generation of logistics optimization software — combining AI, simulation, and human expertise to reduce cost, emissions, and complexity in global supply chains.

You’ll work on hard problems , with real‑world impact , alongside a team that values design‑led thinking, technical depth, and autonomy .

• Strong fullstack engineer with deep backend capability
• Production experience with Python and/or TypeScript
• Experience owning systems from concept to production
• Comfortable working in small, high‑ownership teams

• Hands‑on experience with: LLMs and modern AI tooling RAG systems, embeddings, vector search Agentic patterns (tools, planners, evaluators)
• LLMs and modern AI tooling
• RAG systems, embeddings, vector search
• Agentic patterns (tools, planners, evaluators)
• Experience productionising ML/AI systems (monitoring, failure modes, iteration)
• Pragmatic mindset: understands where AI adds value — and where it doesn’t

Ways of Working

• Agent‑first, spec‑driven , or simulation‑led mindset
• Comfortable operating with ambiguity
• Strong collaborator with Product, Design, and other engineers
• Bias toward shipping , learning, and iterating

• Experience with logistics, optimization, or simulation‑heavy domains
• Background in data engineering or ML platforms
• Experience building developer tools or internal platforms
• Exposure to regulated or security‑conscious environments

• Backend: Python (Django / FastAPI), TypeScript (Node.js)
• Frontend: React
• AI: LLMs, RAG pipelines, agent frameworks
• Infra: Cloud (AWS / GCP), Docker, CI/CD, IaC
• Data: SQL, event‑driven systems, analytics pipelines

We embrace flexibility and hybrid work opportunities to support diverse needs and lifestyles, while also valuing inclusive workplace experiences.

By fostering a sense of community, we drive innovation, strengthen connections, and nurture belonging.

Our commitment ensures you can work in a way that suits you best, while also engaging with colleagues to share ideas and build meaningful relationships.