- Company Name
- Moxie
- Job Title
- Lead AI Engineer
- Job Description
-
**Job title:** Lead AI Engineer
**Role Summary:**
Senior independent contributor leading the design, implementation, and production deployment of agentic AI systems that power customer‑facing products and internal operations. Responsible for establishing architectural standards, validation practices, and scaling strategies for AI across the organization.
**Expectations:**
- 8+ years of building and scaling complex software systems with clear production ownership.
- Proven experience deploying and operating LLM‑based AI solutions in production, not just prototypes.
- Deep understanding of agentic system design: tool use, state/memory management, guardrails, failure handling, and observability.
- Strong software engineering fundamentals: API design, data modeling, testing, and system architecture.
- Ability to own end‑to‑end product lifecycles from design to monitoring and iteration.
- Effective collaborator with senior product and engineering leaders; skilled in communicating technical trade‑offs.
- Bonus: background in regulated or trust‑sensitive domains (e.g., healthcare, fintech) and setting technical standards across teams.
**Key Responsibilities:**
- Design and ship high‑quality, production‑grade agentic AI systems that enhance customer workflows and internal processes.
- Define and document the core AI architecture: agent orchestration, tool/function calling, memory, safety guardrails, and observability.
- Develop robust evaluation, testing, and monitoring pipelines to measure AI performance and drive continuous improvement.
- Integrate AI services securely, cost‑effectively, and with compliance in mind across the platform.
- Establish technical foundations for the AI function, including coding standards, documentation, and mentorship for other engineers.
**Required Skills:**
- Expertise in large language model (LLM) deployment and operationalization.
- Knowledge of agentic AI design patterns: orchestrators, memory/storage, safeguard mechanisms.
- Proficient in cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Strong API design, data modeling, and testing practices.
- Experience with monitoring, logging, and observability tools (Prometheus, Grafana, ELK stack, etc.).
- Excellent problem‑solving, communication, and stakeholder‑management skills.
**Required Education & Certifications:**
- Bachelor’s degree in Computer Science, Engineering, or a related technical field (Master’s preferred).
- Professional certifications in cloud platforms (e.g., AWS Certified Solutions Architect, GCP Professional Cloud Architect) are advantageous but not mandatory.