Engineering Lead (Full Stack+AI)

• Additional Information

Exp: 14-16 Years

Shift Timings: 06.00 P.M. to 03.00 A.M (IST)

Location: Remote

Notice Period : Immediate to 15 days only

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
• Preferred Qualifications
• 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