Engineering Lead (AI & Automation Products)

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 doesnt match the requirement or the architecture is wrong for the problem

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 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