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)

📌 C G Vak Softwares (Bengaluru)
🏢 CG VAK Software & Exports
📍 Bengaluru