Agentic AI Principal Engineer

Role Purpose

Our organization is at the forefront of reinventing education through AI. We are a high-growth, mission-driven startup where your work directly impacts teachers, students, and entire school systems.

As our Principal AI Architect, you will design, build, and optimize the core agentic reasoning systems that power our product. You will work cross-functionally with product, UX, and systems teams to align technical implementations with real classroom outcomes. You will partner with the CTO to define ourAI strategy and IP moat, collaborate with the CPO and Head of UX to shape how personalization and interventions manifest in teacher and student experiences, and work closely with the Systems Architect to ensure scalable, reliable deployment of AI pipelines.

This is a rare opportunity to help pioneer GenAI-based agentic, causal, and reinforcement learning systems in a production-grade education platform — taking state-of-the-art research and making it real for classrooms around the world.

Key Responsibilities

• Architect and implement our agentic AI stack, contributing to the end-to-end system design for multi-agent reasoning.
• Design and orchestrate multi-agent or agentic architectures that deliver measurable improvements in teaching productivity and student mastery velocity.
• Advance automated reasoning using GenAI/LLMs, causal AI, and RL/DRL — optimizing for low latency, safe outputs, and learning outcome alignment.
• Define and own the data strategy — transcripts, curriculum standards, chat interactions, feature stores, experimentation frameworks, and model retraining pipelines.
• Ensure fairness, transparency, and explainability using causal inference, counterfactual analysis, and model interpretability frameworks.
• Collaborate with UX, product, and DevOps teams to translate AI outputs into practical interventions and production-ready pipelines.

Required Experience

• Education: PhD or MS in Computer Science, Data Science, Machine Learning, or related field.
• 3 years of experience delivering ML/AI systems from research to production at scale  
• Demonstrated experience with (two or more) agentic platforms – LangChain, LangGraph, Haystack Agents, Autogen, CrewAI, or LlamaIndex (or equivalent) to build multi-agent reasoning or workflow orchestration systems.
• GenAI / LLM Platforms: Hands-on with OpenAI (GPT), Google Gemini, Anthropic Claude, Mistral, Llama, or open-source LLM fine-tuning (LoRA/QLoRA, PEFT).
• Experience with reinforcement learning tools (e.g. Ray RLlib, Stable Baselines3, CleanRL, TRL) or custom policy-gradient pipelines for bandits, RLHF, or RLAIF.
• Skilled with DoWhy, CausalML, SCM frameworks for counterfactuals, interventions, and causal inference.
• Strong knowledge and working experience with Linux distributions (e.g., Ubuntu, Debian, RHEL, NixOS) and familiarity with Windows OS / MacOS (client-side)
• Solid programming and DB skills: Python, React, Node.js, Java, Json, SQL, NoSQL
• Production experience deploying MLOps with CI/CD, monitoring (Prometheus/Grafana, Sentry, PostHog), model versioning, and drift detection.
• Success building and shipping AI systems that were adopted by real users (commercial, research, or open-source impact all considered).

Bonus points:

• Startup experience is a preferred
• Passion for transforming education and closing equity gaps.
• Experience balancing innovation with model safety, bias mitigation, and regulatory compliance (FERPA, COPPA, GDPR).