Engineering Lead (AI & Automation Products)

Job Description:

Location: Location: DGS – India (overlap hours with US Eastern Time required)

Required Qualifications

• 12–16 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