Engineering Lead (AI & Automation Products)

Job Description:

• 1216 years of professional software engineering experience with deep Python expertise
• Demonstrated experience leading or managing a team of engineers code review mentoring growth planning not just individual contribution
• Strong experience designing and building production APIs in Python (FastAPI Flask or similar) and full-stack applications including React Tailwind CSS front ends
• Strong relational database experience schema design normalization query performance Postgres preferred
• Strong practical proficiency with Claude Code or similar AI-assisted development tools including agentic coding patterns and context management and the ability to establish team standards for effective use
• Experience integrating LLM APIs (Claude OpenAI or equivalent) into production systems system prompt design structured output parsing multimodal input handling
• Practical experience with tool-use/function-calling patterns defining tool schemas validating arguments handling tool results chaining tool calls and managing basic failure/retry behavior
• Strong context engineering fundamentals context window management token budgeting long-document handling strategies and retrieval/context-selection patterns
• Awareness of prompt injection adversarial inputs and untrusted-document risks in AI systems; ability to design guardrails for external briefs trafficking sheets platform exports and other model-readable inputs
• Experience integrating third-party platform APIs with OAuth (any domain) general competency not platform-specific
• Working knowledge of secrets management and credential security practices in production systems ideally including Azure Key Vault or equivalent managed secrets tooling
• Solid grasp of QA practices data quality engineering and AI evaluation: unit and integration testing data validation golden datasets regression evals structured-output checks and observability
• Practical understanding of human-in-the-loop AI systems adjudication workflows labeled examples accuracy measurement by parameter/category feedback loops and quality gates
• Experience with cloud infrastructure (Azure preferred) and modern deployment patterns: containers CI/CD managed identities object storage and background job/workflow execution
• Experience implementing background-processing or workflow patterns queues scheduled jobs retries idempotency status tracking and operational monitoring
• Strong written and verbal communication for collaboration across distributed onshore (US) and offshore (India) teams

• Exposure to LLM application and workflow frameworks beyond raw API calls: LangChain LangGraph CrewAI Temporal Azure Durable Functions Celery/RQ or equivalent agent/workflow tooling useful as the portfolio expands into durable multi-step automation in later phases
• Exposure to model selection and cost optimization strategies prompt caching batching tiered model selection by task complexity latency/cost tradeoff analysis and usage forecasting
• Background in media advertising or marketing technology data environments
• Exposure to data governance tooling such as Unity Catalog attribute-based access control or tag-driven policies
• Exposure to MCP servers or MCP-based developer workflows with interest in when MCP is preferable to direct APIs for reusable tools resources prompts and agent context
• Exposure to data flywheel concepts labeled corpora adjudication data models feedback capture quality dashboards and mechanisms that improve future AI behavior and inform phase-gate decisions
• Exposure to DV360 SDF (Structured Data Files) TTD API or comparable adtech platform data formats/APIs
• Open-source contributions or public projects demonstrating full-stack or AI engineering work

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