Role summary
Hands-on technical leader responsible for turning ambiguous platform concepts into working services, APIs, schemas, workflows, standards, and reference implementations. This is a build-first leadership role for a senior engineer who can set architecture direction while still writing and reviewing production-grade code.
Key responsibilities
- Define platform architecture, service boundaries, API contracts, schema standards, versioning patterns, and release-readiness expectations.
- Build reference implementations for registry services, package/evidence state, review workflows, and platform integration patterns.
- Guide backend, AI-platform, DevSecOps, full-stack, and frontend engineers through design reviews, code reviews, and technical decision-making.
- Establish engineering standards for repository structure, testing, documentation, observability, workflow state, and secure delivery.
- Translate product and architecture intent into executable implementation plans without over-engineering or waiting for perfect requirements.
- Partner with release and security engineering to ensure deployment, rollback, observability, and policy gates are built into the platform from the start.
Required technical skills
- Demonstrated principal or lead engineer scope, including ownership of a production platform or major platform component end to end.
- Strong backend/platform engineering depth using Python, FastAPI or comparable API frameworks, typed schemas, and service design.
- Strong database and state modeling experience, preferably with PostgreSQL or comparable relational systems.
- Experience with lifecycle state, approvals, versioned records, audit trails, workflow orchestration, and operational reliability.
- Ability to create clear engineering standards and help a small senior engineering pod move quickly without creating chaos.
- Strong written communication: can produce architecture notes, ADRs, API standards, review guidance, and implementation plans.
AI / LLM readiness and prompt-coding expectations
- Hands-on prompt coding is required: system prompts, structured-output prompts, reusable workflow instructions, tool-use prompts, evaluation prompts, and prompt versioning.
- Claude Certified Architect, Foundations is preferred where accessible; equivalent demonstrated knowledge is acceptable.
- Expected knowledge areas include agentic architecture, MCP-style tool integration, Claude Code or comparable AI coding workflows, structured outputs, context management, AI skill design, and reliability patterns.
- Experience with Anthropic Claude, OpenAI, or comparable LLM platforms is acceptable; candidates should not be screened for a single vendor only.
Preferred skills
- Temporal or comparable durable workflow systems.
- LangGraph or comparable bounded agent/workflow frameworks.
- OPA/Rego or policy-as-code exposure.
- OpenTelemetry, AI tracing, evaluation, release gates, or platform observability experience.
- Familiarity with data engineering workflow ecosystems is helpful, but this is a platform engineering role rather than a data warehouse delivery role.
First 90-day outcomes
- Publish the first platform architecture and repo/service boundary map.
- Deliver a working reference slice for registry APIs, package/evidence state, and review workflow integration.
- Define the first engineering standards pack, review rubric, and implementation patterns for the pod.
- Help calibrate technical interviews and onboarding expectations for the rest of the engineering team.
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