Education & Experience

BS in Computer Science or equivalent practical experience. 6-9 years of experience building and operating distributed systems. Proven experience delivering cloud-native platforms used in production.

Core Technical Skills

Strong proficiency in Python and shell scripting. Hands-on experience with Kubernetes and container orchestration. Experience building distributed systems with high availability requirements. Strong Linux, networking, and system troubleshooting skills.

AI Platform Knowledge

Experience supporting model training and serving pipelines. Understanding of LLM, ASR, and TTS serving patterns. Experience with prompt engineering and prompt lifecycle management. Familiarity with vector databases and retrieval-based AI systems. Ability to evaluate AI system performance from a platform perspective.

 

Preferred Qualifications

MS in Computer Science or related field. Production experience on OCI, AWS, or Azure. Experience with LLM serving frameworks (KServe, Triton, Kubeflow, etc.). Experience with model optimization technologies (DeepSpeed, FasterTransformer). Familiarity with deep learning frameworks (PyTorch, TensorFlow, JAX). Experience building conversational voice AI systems (ASR, TTS, NLP). Exposure to AIOps or AI-driven observability solutions.

 

IC4 Scope Clarification

Owns design and implementation of AI infrastructure systems. Leads technical initiatives without people management responsibility. Influences architecture within a defined platform area. Bridges applied science and production engineering.