Solution Architect - Agentic Solutions

• Solution Architect
  - Agentic Solutions (Switzerland / Germany)

At Iris.ai , we’re building an agentic AI platform that scales expert-level domain knowledge across entire organizations.

For more than a decade, we’ve worked at the intersection of scientific research, industrial data, and applied AI, helping researchers, engineers, and business teams reason over complex technical knowledge.

Our products
- Neuralith
, Axion
, and RSpace - span the full GenAI lifecycle:

Data ingestion across text, tables, figures, and technical formats

Advanced RAG and indexing pipelines

Agentic orchestration and reasoning

Rigorous LLM evaluation and governance

What makes us different: we care deeply about accuracy, evaluation, and responsibility. We don’t optimize for demos and proof-of-concepts we optimize for systems that experts trust and use.

As a Solution Architect , you’ll join a remote-first, AI-native team focused on building production-ready systems powered by RAG, LLMs, and AI agents. You will play a pivotal role in shaping next-generation AI Agents that bring context-awareness, accuracy, and complex reasoning to real-world problems. You will work alongside our commercial, product and development teams to design, prototype, and deploy intelligent systems – leveraging our Neuralith, Axion, and RSpace products.

Your focus will be to help leading Enterprise organizations find ways to apply Iris.ai's technologies to reach value generation in the shortest path possible.

Design end-to-end Agentic solutions based on Iris.ai ’s products for our Enterprise clients: design modular pipelines that combine Data Ingestion, RAG, LLM evaluation, and autonomous decision-making into cohesive workflows that bring direct value to the client.

Rapid prototyping - rapidly spin up proof-of-concepts, run benchmarks on accuracy and reasoning performance, and iterate based on real-world data with the client.

Integration and deployment - you will collaborate with the Tech team to deliver, orchestrate, and monitor AI Agents in production environments.

Engage in sessions with the Client - you will translate customer requirements into technical designs, run technical sessions with the Client, and provide best-practice guidance for AI adoption.

Collaborate in a cross-dimensional team. Work closely with sales managers, product managers, engineers, designers and researchers to align architecture with product vision and user needs.

Drive innovation by testing new approaches with the Clients and bringing new ideas to the product team.

Focus client systems on performance and reliability, ensuring they are scalable, testable, and maintainable.

Thrive in a remote-first, agile environment, contributing meaningfully within a distributed, deep-tech team.

Keep learning, always — we’ll support you with space, mentorship, and resources to grow as an AI engineer.

Rapid prototyping tools (AI-Assisted Coding
- Windsurf, Cursor, Flowise, Lovable, etc.).

Retrieval-Augmented Generation (RAG) pipelines (ChromaDB, Sentence Embeddings, LLMs).

AI Agent frameworks (Autogen).

Git & CI / CD.

5+ years in client facing product oriented jobs – ideally in AI/ML or data-driven platforms.

Deep understanding of LLMs & RAG – 1+ year hands-on experience (prompt engineering / retrieval pipelines / evaluation metrics).

Strong software engineering skills: proficient in Python, API design, and microservices.

Cloud proficiency: designing and operating scalable services on AWS, GCP, or Azure.

Solid experience in web development (Django/Flask/REST).

Excellent communication: able to convey complex technical concepts to both technical and non-technical stakeholders.

Problem-solver mindset: keen analytical skills, resourcefulness, and a drive to tackle ambiguous challenges.

Ability to work in a remote-first, collaborative environment.

Proficiency in English.

Advanced degree in Computer Science or related field.

Previous work with LLMs, NLP, or AI model evaluation.

Certification in cloud computing.

Previous experience with deployment of RAG solutions in scalable…