[QUAVEO] AI Engineer (Junior/Middle Level)

General Information:

As an AI Engineer, you will be responsible for building, deploying, and maintaining production-ready AI systems. Your role is to bridge the gap between experimental models and real-world applications. You will focus on the engineering challenges of AI–integrating Large Language Models (LLMs), optimizing cloud infrastructure, and ensuring that our AI services are scalable, reliable, and high-performing.

Core Responsibilities:

• End-to-End Development: Contribute to AI-related projects from requirements gathering to delivery for internal and external stakeholders.
• Productionization: Transform experimental scripts and prototypes into robust, production-grade microservices and APIs.
• Generative AI & LLMs: Implement and optimize LLM-based solutions (e.g., RAG pipelines, fine-tuning, prompt engineering) to solve complex business tasks.
• Cloud Infrastructure: Deploy and manage AI workloads on cloud platforms, utilizing managed services for scaling and monitoring.
• System Integration: Collaborate with Backend and DevOps teams to integrate AI components into the broader software ecosystem.
• Performance Optimization: Monitor and optimize model latency, throughput, and cost-efficiency in production environments.
• Technical Exploration Conduct research and exploration to expand technical knowledge, sharpen problem-solving skills, and stay current with cutting-edge technologies.

Required Skills & Qualifications:

• Education: Bachelor’s degree in Computer Science, Information Technology, AI Engineering, Mathematics, Statistics, Data Science, or a related field.
• Engineering Excellence: Strong proficiency in Python and/or C++ , with a deep understanding of software design patterns, APIs (FastAPI/Flask), and asynchronous programming.
• LLM Stack: Proven experience building and deploying applications using LLM frameworks and a deep understanding of vector databases for efficient data retrieval.
• Core Competencies:
• Ability to Learn: Rapidly adapting to the fast-evolving AI landscape (e.g., new LLM architectures or deployment tools).
• Critical Thinking: Evaluating the trade-offs between different engineering approaches (e.g., Build vs. Buy, Local vs. API-based models).
• Attitude: Proactive ownership of the “production” state; you care as much about uptime as you do about model accuracy.
• Evaluation Skills: Ability to develop benchmarks for evaluating AI system performance in real-world scenarios.
• Communication: Strong communication skills with the ability to work in a collaborative environment. Working proficiency in English is required for daily tasks.

Preferred Qualifications:

• Holding a Master or PhD degree.
• IELTS 6.0 / TOEIC 700 equivalent or higher.
• Contribution to open-source projects or a portfolio demonstrating diverse DS/AI interests.

Experience Level Guidelines

• Junior: 1–3 years; focus on API development, basic cloud deployment, and learning the LLM ecosystem.
• Middle: 3–5 years; focus on building end-to-end AI products, optimizing cloud costs, and implementing RAG/LLM pipelines.