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

- Robust 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

- Solid written and verbal communication for collaboration across distributed onshore (US) and offshore (India) teams

📌 Generative AI Engineer (India)
🏢 Deltacubes
📍 India