• Build AI on top of our cloud closing agent pronto • Create connectors and integrations that make company data available to AI systems (Google Workspace, Slack, Jira, GitHub, Snowflake, Confluence and more) • Build and maintain knowledge base pipelines, MCP integrations and API connections that power AI tooling across the business • Work with security and data governance requirements to ensure integrations are safe and appropriate • Support internal teams to create their own AI-powered data sources, automated workflows and internal tools using rapid app builder tools • Build templates, guardrails and building blocks that make it easy for non-engineers to experiment safely • Contribute to our internal automation platform using tools like AWS Bedrock, n8n and custom-built solutions • Work closely with the PM and engineering lead to identify the highest-leverage opportunities • Ship quickly, measure outcomes (time saved, errors reduced, adoption) and iterate based on what you learn • Stay curious about emerging tools and techniques — and apply them where they'll genuinely make a difference to our engineering output

Role Description Our client, a forward-thinking technology organization, is seeking an experienced Generative AI Engineer to join their team as an Instructor. This role involves not only developing innovative generative AI models and solutions but also training and mentoring teams or clients on the implementation and best practices of generative AI technologies. - Design, develop, and implement generative AI models and applications using state-of-the-art techniques. - Deliver training sessions, workshops, and tutorials on generative AI concepts, tools, and best practices to engineers, data scientists, and other stakeholders. - Create comprehensive instructional materials, including manuals, slide decks, and hands-on exercises. - Stay current with latest research and advancements in generative AI, NLP, and related technologies. - Collaborate with cross-functional teams to integrate generative AI solutions into product pipelines. - Provide technical guidance and support to teams adopting generative AI technology. - Evaluate and optimize existing AI models for performance, scalability, and robustness. - Assist in curriculum development for internal training programs related to AI and machine learning. Qualifications - Advanced degree (Master’s or PhD) in Computer Science, Artificial Intelligence, Machine Learning, or related fields. - Bachelors with more than 6-8 years experience. - Proven experience in developing generative AI models using frameworks such as TensorFlow, PyTorch, or similar. - Strong knowledge of NLP techniques, transformer architectures (e.g., GPT, BERT), and generative modeling. - Experience in instructional design and delivering technical training or workshops. - Proficiency in programming languages like Python and relevant AI/ML libraries. - Excellent communication and presentation skills, with ability to convey complex technical concepts to diverse audiences. - Familiarity with cloud AI platforms (AWS SageMaker, Google AI Platform, Azure ML) is a plus. Requirements - Experience with prompt engineering and fine-tuning large language models. - Background in developing AI products or solutions in a commercial environment. - Strong teamwork and mentoring abilities. - Active contributor to AI research or open-source projects. Benefits - Part time. - Pay depends on experience.

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