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

Engineering Lead (Full Stack+AI)