• Build, optimize, and deploy scalable AI capabilities—including Large Models (LLMs), computer vision systems, or generative AI applications—into our core product architecture.
• Collaborate closely with Data Scientists to transition prototypes into robust, low-latency microservices.
• Seamlessly integrate machine learning models into production software environments, utilizing containerization (Docker, Kubernetes) and microservice architectures (FastAPI, Flask).
• Design, implement, and optimize retrieval-augmented generation (RAG) pipelines, fine-tune open-source models, and manage prompt engineering architectures.
• Build and maintain automated machine learning pipelines (MLOps) for continuous integration, continuous deployment, and automated retraining of production models.
• Quantize, cache, and optimize models for high throughput and ultra-low latency, minimizing compute costs and resource footprints.
• Implement data drift detection, model performance tracking, and robust logging structures to ensure system reliability post-deployment.

Bachelor's Degree in Computer Science, Software Engineering, or a heavily technical discipline (or equivalent practical experience) Working explicitly as a Software Engineer or DevOps Engineer in a production-tier AI environment for 1 year