Engineering Lead (AI & Automation Products)

Job Summary

Required Qualifications

• •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
• •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
• •Location: Bengaluru
• •Time Type: Full time
• •Contract Type: Permanent