Role Description 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 others) - Git & CI / CD Qualifications - 5+ years in software development, including 3+ years with Python - Solid experience in web backends (Django/Flask), REST APIs, databases, and cloud infrastructure - Familiarity with ML systems or motivation to learn in depth - Ability to work in a remote-first, collaborative environment - Proficiency in English Any Of These Would Be An Advantage - Advanced degree in Computer Science or related field - Previous work with LLMs, NLP, or AI model evaluation - Experience contributing to or using RAG systems in production - Open-source contributions, academic research, or mentoring Benefits - 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 Compensation & Ownership - 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. - Stock options build wealth! At Iris.ai all colleagues receive ownership in the company, part of our ESOP pool (3%).

Role Description Senior ICs who build ARIP's 15 named agents (A-11..A-25) end-to-end on LangGraph / CrewAI: - Prompt design - Tool definitions - Multi-step workflows - Eval harnesses (golden sets, regression gates, LLM-as-judge, multi-step replay) - HITL gate integration - Trust Gate progression - Per-agent cost optimisation Distinct from DEAA's Senior AI Engineer who owns the LLM Gateway — ARIP AI Engineers are platform consumers and agent builders. Remote candidates outside of Thailand are welcome to apply. Qualifications - 5+ years software engineering - 2+ years shipping LLM-based / agentic systems to production (not just RAG demos or notebooks) - Expert in production multi-agent orchestration: LangGraph / CrewAI / AutoGen / DSPy or equivalent with HITL gates by default, not autonomous-by-default - Eval-driven LLM development in production: golden sets, LLM-as-judge, multi-step replay, regression gates in CI - HITL gate and agent guardrail design: prompt injection / PII / output filtering defences — designs and tests them in production - Strong Python (async, observability, testing) - Major LLM provider (Azure OpenAI / Anthropic / Bedrock / Vertex) production experience - Langfuse or equivalent for LLM tracing - Calibre: Senior AI Engineer from agentic-AI startups (Anthropic-adjacent ecosystem), Agoda, LINE MAN Wongnai, Grab, SCBX with multi-agent production experience Requirements - Build agents on Layer 4 runtime end-to-end — each ships with eval harness, HITL gate config, observability instrumentation, per-agent cost meter, and runbook - Design and own golden-set test cases per agent; build regression gates in CI (no agent ships without eval-pass); implement multi-step conversation replay and LLM-as-judge patterns - Configure per-agent HITL gates and collect gate-progression evidence (Shadow 60d → Recommender 90d → Executor); co-own Trust Gate Framework for Suite 3 financial-threshold ladder (G0–G4) - Tune model routing per agent (LLM provider / model tier): balance cost, latency, quality; implement semantic caching where appropriate - Consume DEAA's LLM Gateway via standard SDK; provide per-agent cost data to DEAA's GenAI Cost Dashboard; partner with DEAA Senior AI Engineer on embedding model selection and retrieval relevance - Author agent-engineering playbook alongside DEAA's AI Best Practices Playbook; mentor PACE-seeded engineers on agent engineering discipline Company Description

• Design, develop, and deploy scalable AI-powered applications and services. • Translate ambiguous business problems into robust technical solutions. • Build and maintain backend systems, APIs, integrations, and AI workflows. • Collaborate across frontend, backend, cloud, and AI initiatives to deliver end-to-end solutions. • Rapidly prototype and iterate on AI-driven products and internal accelerators. • Contribute to architecture decisions, engineering standards, and reusable system patterns. • Mentor junior engineers and support technical growth across the team. • Drive improvements in scalability, reliability, performance, and developer experience. • Work closely with stakeholders to balance business needs, technical trade-offs, and delivery timelines. • Continuously evaluate and adopt emerging AI tools, frameworks, and engineering practices.

• Collaborate with cross-functional teams to design and build AI-powered applications and internal platforms. • Contribute to backend services, APIs, integrations, and frontend components when needed. • Support the development and deployment of scalable AI and LLM-based solutions. • Translate business and technical requirements into functional solutions. • Rapidly prototype, test, and iterate on new ideas and AI use cases. • Work in fast-changing environments with evolving priorities and technologies. • Contribute to reusable engineering patterns, internal tooling, and shared components. • Participate in code reviews, technical discussions, and collaborative problem-solving. • Learn and adopt new frameworks, cloud technologies, and AI tools as required by the project. • Maintain high standards for code quality, collaboration, and continuous improvement.