AI Native Engineer

Responsibilities:

• AI-Driven Development: Write clean, efficient code (Python, Java, or Go) and create unit tests/documentation using AI coding companions.
• Feature Ownership: Own complete features of AI tools, such as building chatbot interfaces for internal policy documentation.
• AI Architecture: Set up RAG pipelines (vector DBs and embeddings) and design end-to-end architecture for mid-sized AI applications.
• Model Optimization: Assist data science teams with fine-tuning jobs (LoRA/QLoRA) and optimize inference latency to ensure high-performance applications.
• Model Strategy: Evaluate and select the right models (commercial vs. open source) based on cost and performance trade-offs.
• Deployment and Debugging: Deploy services using internal platforms (MLP) or cloud-native tools (Docker/K8S) and perform AI-driven root cause analysis (RCA) on production issues.
• Mentorship: Lead and mentor grade 8/9 engineers on AI-native workflows and best practices.

Requirements:

• Education: Bachelor's or Master's degree in computer science, engineering, or a related technical discipline.
• Experience: 5-8 years of professional experience with a proven track record of shipping ML/AI-powered applications.
• Technical Depth: Deep understanding of system design and ML serving infrastructure.
• Proficiency in building integrations with LLM APIs and advanced prompt engineering.
• Hands-on experience with vector databases and the RAG (Retrieval-Augmented Generation) stack.
• Soft Skills: Strong communication and analytical skills, with the ability to bridge the gap between complex AI concepts and practical software implementation.