Engineering Lead - AI & Automation Products

Technical Architecture & Delivery

• Lead architecture decisions across the portfolio — schema design, API contracts, integration patterns, workflow orchestration, and when to integrate via direct API versus MCP (Model Context Protocol), weighing latency, control, security, auditability, and reusability tradeoffs

• Hold delivery accountability — scope, timeline, quality — across all active workstreams, in partnership with the Director who owns product requirements and prioritization

• Design and own the Postgres data schema underpinning the product (multi-table: pipeline data, access control, cost attribution, audit) — correctness and extensibility here is foundational, not incidental

• Build and own the Claude/API integration layer: system prompt design and testing, multimodal document ingestion for XLSX, DOCX, PPTX, and PDF via reliable extraction/conversion pipelines, structured output parsing, schema validation, and fallback handling

• Design practical automation workflows — background jobs, queues, retries, idempotency, human review points, run state, and replayable audit history for multi-step AI-assisted processes

• Build and own third-party DSP API integrations (e.g. DV360, TTD) — OAuth flows, structured data file formats, read/write scoping by phase

• Design and own secrets and credential management — API keys, OAuth credentials, Azure Key Vault or equivalent secrets manager; nothing in code or environment variables in source control

• Design and own multi-tenant access control — role-based permissions, client-scoped data visibility, self-service onboarding

• Design cost and usage attribution patterns — external API calls tagged and logged at the client and run level, feeding finance and program-level reporting as the portfolio scales

• Review technical output against product requirements — flag when implementation doesn't match the requirement or the architecture is wrong for the problem

Team Leadership

• Manage a team of junior and mid-level developers based in India — hiring input, onboarding, performance, growth planning

• Run code reviews, pairing, and technical mentoring as a standing practice, not an occasional one

• Set and enforce engineering standards: testing discipline, data validation, AI evaluation discipline, observability, and code quality bar

• Standardize how the team uses Claude Code — establish shared conventions, prompt/context patterns, and reusable practices so the whole team improves its delivery quality, not just you

• Run sprint-level technical planning and unblock the team day to day; escalate cross-team blockers to the Director rather than letting them stall delivery

Build Execution

• Build full-stack applications end to end where needed: Python APIs (FastAPI or similar) and React/Tailwind front ends

• Use Claude Code as a daily driver — both for personal output and as the standard tool your team is held to

• 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 practical evaluation harnesses for AI behavior — golden test sets, prompt/model versioning, regression checks, failure taxonomies, and release gates for higher-risk automations

• Contribute to reusable component libraries and shared platform services that scale across clients and future portfolio products