Principal Machine Learning Engineer - Artificial Intelligence

Role:

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

Key Responsibilities:

• 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:

• Build and deploy production-grade Machine Learning and Transformer models, from data preparation and training to model serving, monitoring, and continuous improvement.

• Develop and optimize Large Language Models (LLMs) using Hugging Face, PyTorch, LoRA/QLoRA, embeddings, quantization, and inference optimization techniques.

• Design scalable ML platforms with data pipelines, training pipelines, MLOps, Docker, Kubernetes, Spark, Airflow, Kafka, and MLflow.

• Build high-performance AI applications using Python, FastAPI, REST/gRPC APIs, real-time and batch inference, distributed systems, and model serving frameworks.

• Lead ML system design, architecture, technical strategy, mentoring, and end-to-end delivery of scalable production AI solutions.

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.