Lead AI Engineer

4 weeks ago

Bengaluru, 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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