Lead AI Engineer

6 days ago

New Delhi, India PetYosa Full time

Bangalore | Full-Time | ₹20–30 LPA | Min. 4 Years of Experience | Hands-On RoleWe are building a new kind of intelligence platform that sits at the intersection of health, behaviour, wearables, and real-time decision systems. Our data pipeline captures 60,000+ data points per pet per day across motion, GPS, audio, health logs, and behavioural events.We are looking for a founding AI engineer who can own the end-to-end AI architecture — not just build models, but productionize them at scale with real-world impact.What You’ll BuildYou’ll be responsible for architecting and shipping:- Full-stack AI/ML pipelines to handle real-time multimodal pet data - Time-series models to detect early illness, restlessness, aggression, and sleep anomalies - Smart nudges and personalised pet recommendations (movement, health, care) - Behavioural clustering and risk scoring engines (for insurance, hosts, vet dashboards) - Embedding models powering our internal PetAI assistant - Data architecture for structured + unstructured pet telemetry and app activity - Future integrations: on-device/edge inference for our smart collarWho Uses Your Work- Pet parents (daily insights and warnings) - Vets (medical context and pre-visit summaries) - Groomers and hosts (temperament tagging) - Internal AI assistant (PetAI) - Ops and support teams (incident prediction) - Founders and BI teams (trend dashboards, retention analytics) - Future insurance and ecosystem partners (risk engines)What We’re Looking For- Minimum 3 years of hands-on experience in ML/AI engineering - Strong foundation in time-series modeling, signal processing, or real-time inference - Experience with tools like PyTorch, TensorFlow, Scikit-learn - Knowledge of embeddings, retrieval pipelines, vector DBs - Data infrastructure fluency: streaming (Kafka), orchestration (Airflow), DBs (Postgres) - Ability to build for scale, not just hack demos - Prior experience working with IoT or health/behavior data is a strong plus - Bonus: Edge ML, on-device inference, LLM integrations - Must be comfortable working independently in a fast-moving startup environment

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