
Lead AI Engineer
6 days ago
Bangalore | Full-Time | ₹20–30 LPA | Min. 4 Years of Experience | Hands-On Role
We 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.
You’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 collar
- 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)
- 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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