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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 at Iris.ai, 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.)
Cloud & Infrastructure (AWS/GCP/Azure, Kubernetes, Docker, Terraform)
Retrieval-Augmented Generation (RAG) pipelines (ChromaDB, SentenceEmbeddings, LLMs )
AI Agent frameworks (Autogen)
Machine Learning stacks (PyTorch, TensorFlow, etc.)
Backend & APIs (Python, Django, etc.)
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: profficeint 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 production environment
Open-source contributions, academic research, or mentoring
🌱 Why Join Iris.ai?
If you want to do meaningful work and grow in a culture built on trust, rigor, and fairness — let’s talk.
We’re not your typical tech company. We believe in:
Real transparency — information is shared, context is open, and questions are welcome.
Fairness, designed in policies, opportunities, and growth are aligned across countries and teams.
Ownership and empoweredness to make decisions without micromanagement.
Metrics that guide us — but they never replace human thinking or responsibility
💸 Pay
Compensation that reflects your value. Our salaries are typically 25% above local market averages, ensuring competitive, fair pay across regions and roles. And we review it annually.
💸 Equity
We believe salary helps you get by. Stock options build wealth!
At Iris.ai all colleagues receive ownership in the company, part of our ESOP pool (3%). Because when we grow, you grow — that's what shared success really means.
(Just imagine: Someone once bought a Tesla option for $1 — it's worth $400 today.)
We’ve built our benefits to reflect how we work: with trust, fairness, and room to grow.
30 days paid vacation
5 additional days paid vacation for Learning and Development
Private health insurance (premium coverage) and bi-annual health checks
Free MultiSport card or Fitness subscription coverage for your physical well-being
Remote-first & flexible hours — work where you're at your best
Personal annual learning budget for conferences, courses, or certifications
Personal equipment budget to choose the gear that suits your style
Charity and volunteer activities
Seasonal working camps (summer & winter) and team retreats
Ongoing growth through weekly tech deep dives, mentorship, pair coding, and knowledge-sharing
If you care about building high-quality, ethical AI — guided by data and human judgment — you’ll feel at home at Iris.ai.
👉 Apply now or reach out with questions. We’re transparent by default.