AI Platform Engineer

Location: Remote, Open to all US states; preferred locations: Colorado, Massachusetts, New Hampshire.

Base Salary: $135,400 – $159,500 annualized

Equity: Initial incentive stock option grant (1-year cliff, 4-year vesting).

Benefits: Medical, Dental, Vision, employee-funded 401(k), unlimited PTO, and paid company holidays

Reports to: Chief Product and Strategy Officer

Role Summary: Build the connective layer that turns AI into useful action. You will design, ship, and operate the MCP servers, APIs, agent infrastructure, and backend services that connect Nexus AI to live data and customer workflows. This is a hands-on full stack engineering role contributing to all parts of the Nexus product suite. Your work will be measured by what customers can reliably do with the product. MCP Server & API LayerMCP Servers & APIs: Build MCP servers that give agents live access to data and systems. Design REST API endpoints, gateway layers, and versioned contracts across the stack. Routing Logic: Determine when to leverage RAG, MCP, or fine-tuned models to optimize AI agent performance, accuracy, and the quality of customer-facing responses. AI-Native Platform & Agent EngineeringAI Agent Development: Design, build, and deploy production-ready AI agents that integrate with Nexus data, APIs, and external systems to deliver reliable, customer-facing functionality. Quality & Safety: Build automated evaluation harnesses at customer-safe thresholds and implement human-in-the-loop checkpoints for high-stakes agent decisions. RAG & Data Intelligence Infrastructure End-to-End RAG: Build and maintain RAG pipelines: semantic chunking, embedding model selection, hybrid search, indexing, and automated ingestion as content changes. Vector Store Quality: Monitor retrieval accuracy, tune chunking and embedding parameters, and maintain index quality to keep AI output grounded and low-latency. Backend & Cloud InfrastructureAI Backend Development: Build and maintain Python backend services on GCP that support AI agents, tool integrations, and customer-facing applications. APIs & Webhooks: Develop and integrate REST APIs and external webhooks that allow AI agents to access live data, interact with external systems, and trigger customer workflows. Data Integration: Connect AI agents to existing Nexus data sources and databases, including PostgreSQL, ensuring reliable and secure access to the information they need. DevOps, Platform Reliability & QualityCI/CD & IaC: Apply DevOps practices using Terraform, Kubernetes, and GitHub Actions with AI evaluation harnesses embedded in deployment so quality regressions are caught before production. Platform Monitoring: Monitor MCP server and AI platform uptime, performance, and costs. Respond to degradation proactively. Use GitHub Copilot and Cursor natively as the primary development mode. Technical & Cross-Stack Engineering Code Review & Unblocking: Review pull requests across the full stack and float into frontend, backend, AI, or infrastructure work to pair on hard problems and unblock the team. Standards & Architecture: Define coding standards, API conventions, and architectural patterns. Surface technical debt and architectural risks without waiting to be asked. Metrics for Success Reliable platform performance; accurate, safe agent outputs; successful live-system integrations; retrieval quality and latency; scalable infrastructure costs; and stronger team delivery and code quality. ToolsPython, SQL, MCP, REST APIs, Kubernetes, CI/CD, and AI-assisted development tools such as Cursor or GitHub Copilot, GCP, agent orchestration frameworks, and PostgreSQL/vector retrieval tooling. Core Skills Building Systems: Turn complex needs into useful, maintainable software and automation. Autonomy: Own work independently, make progress without task-level direction, and involve others when needed. Results: Set clear goals and see work through deployment and ongoing operation. Improvement: Identify risks and better ways of working, then implement practical changes. Teamwork: Share context proactively, invest in others’ code, and put team outcomes ahead of narrow ownership. AI Pipeline Management & MCP: Build and operate AI pipelines and MCP servers that connect agents to live data and systems. Python: The universal language of this stack, MCP servers, APIs, RAG pipelines, agent orchestration, backend services, and evaluation scripts all run in Python. Full Stack Development: Fluency across frontend, backend, middleware, AI, and data layers, able to review and contribute to any part of the stack. Kubernetes & Container Orchestration: Daily operational requirement for cloud-native deployment across the platform. REST API Development: Design and maintain versioned REST APIs and tool-use interfaces that expose platform capabilities cleanly across services.

Qualifications: Basic

Experience: 2+ years of AI experience, including shipping and operating agentic systems used by real customers.

Education: Bachelor's degree or equivalent practical experience. Startup Agility: High comfort level building without the resources and history of a more established firm. A self-starter capable of identifying problems and executing solutions without needing a fully defined process but still knows when to communicate. AI Integration & Operations: Hands-on MCP/tool integration and production AI pipeline experience, including automated evaluations and model lifecycle management. Python & Full Stack Development: Strong Python engineering and the ability to build, debug, and review work across frontend, backend, and data layers. Cloud & APIs: Experience building REST APIs and operating containerized, cloud-native applications, including Kubernetes. Product & Data Quality: Experience delivering customer-facing products backed by complex data, with clear ownership of data integrity and output quality.

Preferred: Cloud-Native Architecture: Experience operating cloud-native infrastructure at scale. RAG & Vector Retrieval: Experience building RAG pipelines end-to-end, including semantic chunking, embedding models, vector databases, and hybrid search. Agent Evaluation Tooling: Experience testing and monitoring AI applications to assess response accuracy, reliability, and performance, using evaluation frameworks such as LangSmith, Ragas, Arize Phoenix, or equivalent tools. Technical Domain Background: Experience at a company working in a technically rigorous field; scientific computing, data infrastructure, engineering tooling, or a similarly precision-oriented environment. Early-Stage Engineering: Previous experience as an early engineer at a startup.

Posting Statement: Applications are reviewed on a rolling basis; the application window is anticipated to close on December 9, 2026, but the role may be filled sooner. A Note to Applicants: If you are interested in joining our team and believe you can be successful in the role, we strongly encourage you to apply even if you don’t meet every requirement.

Equal Employment Opportunity & Inclusion: We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, sex, age, disability, or any other classifications protected by applicable law. Know your rights; workplace discrimination is illegal.

Accommodations: We are committed to providing reasonable accommodations to individuals with disabilities throughout the recruiting process. If you require accommodation or adjustment, please contact Matt McGrady (). Work Authorization Candidates must be legally authorized to work in the United States for the duration of their employment. At this time, we do not sponsor applicants for work visas.