Engineering Lead (AI & Automation Products)

About the role

Engineering Lead (AI & Automation Products) at Merkle leads software engineering teams to deliver high-quality AI and automation products. Day-to-day work includes leading architecture decisions across the portfolio, owning Postgres schema design, building Claude/API integration layers, designing automation workflows, and managing third-party DSP API integrations. The role also involves team leadership—hiring, onboarding, mentoring, and setting engineering standards for junior and mid-level developers based in India. Location: Bengaluru, India (also listed: Maharashtra - Mumbai - Thane). Full-time, permanent.

What you’ll do

• Hold delivery accountability — scope, timeline, quality — across all active workstreams, in partnership with the Director who owns product requirements and prioritization
• Design cost and usage attribution patterns — external API calls tagged and logged at the client and run level, feeding finance and program-level reporting
• Review technical output against product requirements — flag when implementation doesn't match the requirement or the architecture is wrong for the problem
• Run code reviews, pairing, and technical mentoring as a standing practice
• 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
• Run sprint-level technical planning and unblock the team day to day; escalate cross-team blockers to the Director
• Build full-stack applications end to end where needed: Python APIs (FastAPI or similar) and React/Tailwind front ends

What you’ll bring

• Manage a team of junior and mid-level developers based in India — hiring input, onboarding, performance, growth planning
• Lead architecture decisions across the portfolio — schema design, API contracts, integration patterns, workflow orchestration, and when to integrate via direct API versus MCP
• Design and own the Postgres data schema underpinning the product (multi-table: pipeline data, access control, cost attribution, audit)
• Build and own the Claude/API integration layer: system prompt design and testing, multimodal document ingestion, 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
• 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
• Design and own multi-tenant access control — role-based permissions, client-scoped data visibility, self-service onboarding

Skills