Role: Your Impact and Ownership
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.
Skill: The Expertise Required
Must-Haves For This Role
- 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.
(ref:hirist.tech)
