Job Title : Video Analytics Data Architect
Location: Kondapur HYD( Hybrid 3 days in a week in office )
Experience: 5-8 years
Role SUMMARY
Own the end-to-end technical architecture of the platform — from IP67 edge camera capture through computer vision inference to the SaaS dashboards used by kitchen operators, aggregators, and FSSAI regulators. This is a hands-on leadership role for someone who has already taken at least 2–3 video analytics products from zero to production, ideally in kitchen, utility, or industrial/environmental-safety domains where wastage, hygiene, and safety KPIs were the core deliverable.
KEY RESPONSIBILITIES
- Architecture ownership — design the full edge-to-cloud pipeline: IP67 camera capture, RTSP ingestion, edge inference nodes, MQTT-based event orchestration, and the cloud aggregation and dashboard layer.
- SaaS platform — direct development of the web application across both frontend and backend, covering the operator dashboard, aggregator API layer, and FSSAI regulator view.
- MLOps & model lifecycle — own model versioning, CI/CD for model deployment, automated retraining pipelines, and continuous production monitoring for drift, accuracy decay, and latency.
- KPI pipelines — define and enforce data-pipeline standards for hygiene, wastage, and environmental-safety KPIs (PPE compliance, cross-contamination, spill/pest detection, portion variance, storage segregation).
- Performance & reliability — set and hold the team to technical SLAs such as sub-2-second inference latency and 99.9% system uptime.
- Cloud & DevOps — evaluate and implement AWS or GCP infrastructure for secure, multi-tenant deployment across hundreds of outlets; build containerization, CI/CD, and observability practices.
- Team leadership — lead and mentor the Data Scientist and the three interns; run technical reviews and unblock delivery.
- Compliance by design — ensure the architecture meets DPDP Act 2023 and FSSAI requirements, including edge-anonymization of facial data for the Smilometer/Trust Score modules.
- Founder support — partner with the founders on technical due diligence for fundraising conversations and enterprise or regulator pilots.
REQUIRED SKILLS & QUALIFICATIONS
- 5–8 years of software/ML engineering experience, including at least 2–3 video analytics projects taken from concept to live production.
- Prior experience in kitchen, utility, industrial, or environmental-safety monitoring domains strongly preferred — especially wastage tracking, hygiene compliance, or safety KPIs.
- Strong full-stack development ability — modern frontend framework (React or equivalent) and backend (Node.js/Python).
- Hands-on MLOps experience: model packaging, deployment pipelines, and monitoring/alerting for model drift.