We are looking for a Machine Learning Engineer with strong expertise (atleast 3 years of experience) in Edge ASR and NLP to work on real-world, production-grade healthcare systems.

This role requires hands-on ownership of multilingual, streaming based and offline speech pipelines, designed for noisy and low-resource, environments and developing healthcare algorithms and production grade systems.

You will work closely with product, backend, and field engineering teams to build systems that directly impact clients.

What You Will Do :

- Design, build, and deploy streaming multilingual ASR systems with low latency and high reliability for edge devices.

- Work on offline and edge-deployable ASR pipelines for real-time usage

- Develop and fine-tune clinical speech recognition models, handling medical vocabulary, code-mixed language, and symptom-driven speech

- Implement background speech isolation, noise suppression, and robust VAD for noisy healthcare environments

- Build and optimize custom offline and edge enabled TTS systems for healthcare prompts and follow-up questions

- Own the end-to-end ML lifecycle: data preparation, model training, evaluation, deployment, and monitoring

- Continuously improve model accuracy using real-world error analysis and feedback loops

- Develop medical extraction models to run on edge systems with low compute resources

- Develop computer vision based models to correlate the medical inputs with captured camera data

- Collaborate across teams and take responsibility for system design and long-term maintainability

What You Will Need :

1. Core ML & Coding Skills: Strong proficiency in Python and deep learning frameworks.

2. Solid hands-on experience in :

- Computer Vision (OpenCV, CNNs, detection/segmentation/OCR etc.)

- ASR / Speech models (Whisper, wav2vec2, NeMo, ESPnet, etc.)

- NLP / Transformers

- RAG, Knowledge Graphs

3. Edge ML & Systems Experience :

- Experience deploying ML models on Android (TFLite / ML Kit / ONNX Runtime Mobile / PyTorch Mobile / ExecuTorch).

- Experience running models on Raspberry Pi or similar SBCs with cameras/mics/sensors.

- Understanding of edge constraints: CPU-only or small GPU, limited RAM, power limits, offline/spotty network.

- Familiarity with techniques like quantization, model size reduction, lower input resolution, mixed precision, etc.

4. Tooling & Infra :

- Comfortable working in Linux environments, SSH, logs, basic networking.

- Experience with Git, basic CI/CD and preferably Docker or similar for packaging services.

- Ability to expose models as APIs (FastAPI/Flask) and debug end-to-end flows.

- Language & Domain Comfort :

- Practical familiarity with at least one Indic language (e.g., Hindi, Gujarati, Telugu, etc.) and willingness to work with noisy, code-mixed speech and regional accents.

- Genuine interest in healthcare / social impact and sensitivity while handling patient data. 5. Mindset & Soft Skills

- Ownership mentality: you enjoy taking things from idea ? implementation ? deployment ? iteration.

- Comfortable with ambiguity, changing priorities, and cross-functional collaboration in a startup. - Clear communicatio