Who Are We?

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.

The Role

As a Senior AI Engineer 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’ll collaborate with research scientists, engineers, and product managers to design and deliver AI features that go beyond generation - helping users navigate, reason, and act on complex knowledge.

You’ll work across the full development lifecycle, from design to deployment, and play a key role in shaping how next-gen AI is evaluated, trusted, and adopted in real-world workflows.

What You’ll Do

• Own the full development lifecycle — from idea to architecture, implementation, testing, and delivery of real-world AI products
• Design and build robust AI systems, including Iris.ai’s proprietary Retrieval-Augmented Generation (RAG) pipelines and LLM Evaluation Frameworks used by leading R&D teams
• Drive innovation by testing new approaches and bringing new AI research into production
• Collaborate across teams — work closely with researchers, product managers, and fellow engineers to shape and ship powerful features
• Build systems for performance and reliability, ensuring they are scalable, testable, and maintainable
• Contribute to a global mission to transform how the world interacts with scientific knowledge and applied AI
• 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

Our Tech Stack

• Python & Django
• Relational databases (PostgreSQL)
• Non-relational and vector databases (Elasticsearch / OpenSearch, ChromaDB, Couchbase)
• AI / ML / NLP libraries (Gensim, Torch, Transformers, spaCy, Autogen, etc.)
• Cloud (AWS)
• GenAI tools (Windsurf, Cursor, Amazon Q, Claude and other)
• Git & CI / CD