Engineering Lead (AI & Automation Products)

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Lead architecture decisions including schema design, API contracts, integration patterns, and workflow orchestration. Hold delivery accountability for scope, timeline, and quality across workstreams. Design and own Postgres data schema for pipeline data, access control, cost attribution, and audit. Build and own Claude/API integration layer with system prompt design, multimodal document ingestion (XLSX, DOCX, PPTX, PDF), structured output parsing, schema validation, and fallback handling. Design automation workflows with background jobs, queues, retries, idempotency, human review points, run state, and replayable audit history. Build and own third-party DSP API integrations (e.g. DV360, TTD) including OAuth flows and structured data file formats. Design and own secrets and credential management using API keys, OAuth credentials, Azure Key Vault or equivalent. Design and own multi-tenant access control with role-based permissions, client-scoped data visibility, and self-service onboarding. Design cost and usage attribution patterns for external API calls tagged and logged at client and run level. Review technical output against product requirements and flag mismatches. Manage a team of junior and mid-level developers including hiring input, onboarding, performance, and growth planning. Run code reviews, pairing, and technical mentoring regularly. Set and enforce engineering standards including testing discipline, data validation, AI evaluation discipline, observability, and code quality. Standardize team usage of Claude Code with shared conventions and reusable practices. Run sprint-level technical planning and unblock the team daily. Build full-stack applications end to end with Python APIs (FastAPI or similar) and React/Tailwind front ends. Write tests, build data validation frameworks, and instrument observability across the pipeline including prompt/model traces, structured-output validation, latency, cost, and failure analytics. Build and maintain evaluation harnesses for AI behavior including golden test sets, prompt/model versioning, regression checks, failure taxonomies, and release gates. Contribute to reusable component libraries and shared platform services.