[Remote] Founding Lead Engineer / Principal Systems Architect

Note: The job is a remote job and is open to candidates in USA. OpenTeams is dedicated to unlocking human potential through AI that empowers rather than drains resources. They are seeking a Founding Lead Engineer / Principal Systems Architect to build an intelligent operations platform focused on evidence-governed analysis and high-integrity reporting in regulated domains, particularly healthcare.

Responsibilities

• 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
• 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
• 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
• 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
• 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
• Deploy services using Docker, Kubernetes, Helm, GitOps, CI/CD, RBAC, secrets management, observability, and secure environment practices
• Support cloud, private-cloud, hybrid, or OpenTeams/Nebari-aligned infrastructure where applicable
• Implement secure configuration, environment promotion, logging, backup/restore, and infrastructure-as-code practices
• Build deployment patterns that can support development, test, staging, and controlled pilot environments
• Build synthetic datasets, golden tests, regression tests, benchmark suites, schema tests, model-output checks, and security-boundary tests
• Validate ingestion throughput, entity-resolution accuracy, graph query performance, model latency, report generation, audit volume, and backup/restore behavior
• Ensure every major module has clear acceptance criteria and reproducible test evidence

Skills

• 8+ years of professional software engineering experience, or equivalent exceptional experience
• Expert-level Python engineering
• Experience building production backend services, APIs, data pipelines, and distributed systems
• Strong SQL and relational database design experience, preferably PostgreSQL
• Experience with graph databases, knowledge graphs, or complex relationship modeling
• Experience with LLM integration, open-weight models, structured outputs, prompt/template management, or model-evaluation workflows
• Experience with Docker, Kubernetes, Helm, GitOps, CI/CD, and secure cloud or private infrastructure deployment
• Experience with data validation, audit logging, RBAC, secrets management, and secure software design
• Ability to design modular systems from ambiguous early-stage architecture
• Ability to translate non-engineering conceptual guidance into concrete software architecture and implementation plans
• Strong written documentation skills
• Comfort working directly with a non-engineer concept architect
• Experience with Nebari, Dask Gateway, Keycloak, or comparable data-platform infrastructure
• Experience with vLLM, KServe, Ray Serve, Ollama, llama.cpp, Hugging Face Transformers, or comparable model-serving infrastructure
• Experience with Neo4j, Cypher, graph analytics, graph ETL, or graph visualization
• Experience with OPA/Rego, policy-as-code, deterministic rule engines, symbolic validation, or explainable decision logic
• Experience with FastAPI, Pydantic, SQLAlchemy, Alembic, pytest, and modern Python service design
• Experience with Terraform, ArgoCD, Flux, Vault, Prometheus, Grafana, OpenTelemetry, or comparable DevSecOps tooling
• Experience with vector databases, hybrid retrieval, pgvector, OpenSearch, Elasticsearch, or comparable retrieval systems
• Experience with Dask, Spark, Kafka, Redpanda, RabbitMQ, or comparable distributed processing and event-streaming systems
• Experience in healthcare, government, legal, finance, cybersecurity, program integrity, or other regulated environments
• Familiarity with provider enrollment, NPI/NPPES, PECOS, LEIE/exclusion references, licensing records, corporate registries, or healthcare integrity workflows
• Familiarity with EDI healthcare transactions, eligibility files, managed-care encounters, FHIR, HL7, or EHR audit logs is helpful for later expansion phases
• Experience building AI systems with human review, auditability, evidence controls, and high-consequence output safeguards
• Experience with private-cloud, on-prem, hybrid, or air-gapped deployments

Benefits

• Equity, performance-based incentives, or founding-team participation may be considered for the right candidate.
• We offer 100% employer paid medical premiums for employees and self-managed PTO with a minimum time off requirement, so that our teams are able to do their best work.

Company Overview