Description :

Own and scale end-to-end ML systems across product lifecycle, partnering with product and engineering teams while mentoring ML engineers.

Key Responsibilities at this Role :

- Lead end-to-end development and deployment of scalable ML products in production environments.

- Design real-time and batch ML serving architectures with monitoring and feedback mechanisms.

- Optimize transformer-based models for inference performance, scalability, and production reliability.

- Drive architecture decisions across data pipelines, training infrastructure, and ML platforms.

- Mentor junior ML engineers through code reviews, technical guidance, and design reviews.

- Collaborate with product and engineering teams to align ML outcomes with business impact.

- Own ML initiatives from ideation through deployment, scaling, and continuous improvement.

Must-Haves for this Role :

- 8+ years of hands-on Machine Learning engineering experience

- Strong Python programming with production-grade software development expertise

- Deep Learning expertise including Transformers, NLP, or Computer Vision systems

- Experience fine-tuning and optimizing large transformer architectures

- Production exposure to scalable ML deployment and monitoring systems

Extra Information :

- Experience with LLMs, GenAI frameworks, or multimodal AI systems is a plus

- Exposure to cloud platforms like AWS/GCP/Azure is preferred

- Prior experience in health-tech or consumer-tech environments is advantageous

- Ability to operate in ambiguous and fast-paced startup ecosystems

- Strong stakeholder communication and technical leadership capabilities

- Expected to influence long-term ML roadmap and engineering standards

- Role based out of Bengaluru with hybrid work flexibility