Founding Lead Engineer / Principal Systems Architect

Who We Are We exist to unlock human potential. Too often, AI drains it—drains budgets, drains energy resources, drains ownership of data. OpenTeams was founded to change that. We build AI that empowers. Our models are energy-efficient, cost-effective, and fully yours.  Our ethos is open source. That means freedom, trust, and accountability are built into every line of code. We reinvest 3% of our profits back into the open-source community, because we believe tech is most powerful when it serves everyone. At our core, we value freedom, teamwork, accountability, and uncompromising quality. If you want to challenge the status quo, and shape tools that set people free, OpenTeams is the place to do it. Founding Lead Engineer / Principal Systems Architect Evidence-Governed AI/Data Platform Location: Remote / Hybrid Employment Type: Full-time Seniority: Principal / Staff-level Experience: 8-12+ years preferred, or equivalent exceptional experience About the Role We are building a confidential intelligent operations platform for evidence-governed analysis, operational reconstruction, model-assisted workflows, and high-integrity reporting in regulated domains. The first deployment focuses on healthcare integrity, provider-level identity mapping, licensing, ownership, source reconciliation, and defensible review workflows. We are seeking a hands-on Founding Lead Engineer / Principal Systems Architect to work directly with the concept architect and translate a large, complex system vision into production-grade software, data architecture, model integrations, validation harnesses, and secure Kubernetes-based deployment infrastructure. This is not a standard software engineering role. This is a founding technical role for building the core architecture of a serious AI/data platform from the ground up. The right candidate must be able to absorb abstract system concepts in real time and convert them into schemas, APIs, service boundaries, deployment artifacts, validation tests, and pragmatic engineering roadmaps. What You Will Build You will lay the technical foundation for a modular, enterprise-scale AI/data platform, including: canonical identity and entity-resolution services; source registry and evidence-management services; provider-level healthcare integrity workflows; relational, graph, object-store, retrieval, and audit data layers; deterministic rules and validation services; model-adapter and multi-model routing layers; structured-output and model-evaluation workflows; human-in-the-loop review workflows; graph, timeline, and evidence-review prototypes; evidence-linked reporting; audit logging and compliance-supporting records; secure Kubernetes / cloud / private-infrastructure deployment; validation, benchmark, and regression harnesses. The first deployment will focus on provider-level healthcare integrity. Future deployments may extend into other regulated and high-consequence domains, including legal, financial, AI governance, cyber, public-sector, operational risk, and training/simulation environments. Key Responsibilities Concept-to-Code Translation Work side-by-side with the concept architect to convert advanced system ideas into technical specifications, service maps, data models, APIs, schemas, tests, and deployment plans. Translate verbal and written design guidance into architecture diagrams, implementation backlogs, acceptance criteria, and working prototypes. Identify ambiguity, missing assumptions, engineering risks, security issues, and implementation conflicts. Help turn an evolving concept architecture into reproducible, testable, maintainable software. Backend and Platform Engineering Build production-grade Python services, APIs, data pipelines, background workers, and orchestration logic. Design clean service boundaries for ingestion, entity resolution, evidence management, review workflows, reporting, audit logging, and model integration. Build deterministic, auditable workflows for high-consequence system operations. Establish repository structure, coding standards, documentation practices, testing standards, and implementation discipline. Data, Graph, and Knowledge Architecture Design and implement relational schemas, graph models, object-storage structures, retrieval indexes, and audit records. Build canonical identity and entity-linking systems that reconcile conflicting real-world records. Support relationship topology, ownership mapping, provider-network analysis, and source-conflict preservation. Implement data validation, source normalization, evidence linking, deduplication, and data-quality checks. AI / LLM Systems Engineering Build a model-agnostic adapter layer for open-weight and hosted models. Implement multi-model routing for parsing, extraction, summarization, evidence explanation, report drafting, reviewer critique, and deterministic no-model workflows. Integrate model-serving infrastructure such as vLLM, KServe, Ray Serve, Ollama, llama.cpp, Hugging Face, or equivalent tools where appropriate. Implement structured outputs, prompt/template management, model-call audit, output validation, and model versioning. Ensure model outputs remain constrained by evidence, rules, schemas, human review, and audit records. Human-in-the-Loop Review and Visualization Build rapid internal UI prototypes for evidence review, graph visualization, timeline inspection, review queues, report review, and audit inspection. Use tools such as Streamlit, Plotly Dash, Retool, React, Next.js, or equivalent frameworks where appropriate. Design backend APIs and data contracts that allow a dedicated frontend or full-stack engineer to later build a production analyst/reviewer workspace. Ensure human reviewers can inspect evidence, source conflicts, model outputs, rule triggers, and report language before high-consequence outputs are finalized. Infrastructure, DevSecOps, an