The Opportunity

We’re looking for ambitious undergraduate new graduates with a strong foundation in computer science and a genuine interest in AI to join us as an Applied AI Engineer. This is an ideal role for someone who has built projects, completed internships, or pursued coursework in backend systems and applied AI—and is ready to grow those skills in a fast-paced production environment. You’ll work alongside senior engineers, product managers, and AI researchers to build systems that drive investigation speed, streamline operations, and unlock new modes of human-AI collaboration.

What You’ll Do

Development

• Build and maintain backend services and AI-integrated features for real-time, high-volume operational workloads.

• Integrate LLMs and GenAI components into production workflows, including prompt orchestration, retrieval pipelines, and evaluation loops.

• Help design data flows (event streams, message queues, job orchestration) to support next-generation SOC/NOC capabilities.

• Write clean, well-documented code with clear API contracts between AI services, backend APIs, and frontend clients.

• Contribute to trustworthy AI delivery: streaming responses, structured outputs, redaction/guardrails, and human-in-the-loop review.

Collaboration

• Work with senior backend engineers to build data pipelines that power AI-driven features.

• Collaborate with design and frontend engineers to translate complex backend/AI systems into intuitive user experiences.

• Participate in code reviews, technical design discussions, and team retrospectives.

• Communicate clearly with both technical and non-technical teammates about progress, blockers, and trade-offs.

Who You Are

Education

• Bachelor’s degree in Computer Science, Computer Engineering, AI/ML, or a closely related technical field (graduated within the past 6 months or graduating by Summer 2026).

Technical Skills

Core Backend & Platform

• Proficiency in at least one modern backend language (e.g., Python, Go) demonstrated through coursework, personal projects, or internships.

• Familiarity with API design (REST, WebSocket, or GraphQL) and basic understanding of event-driven or real-time systems.

• Coursework or hands-on exposure to relational databases (e.g., Postgres) and data modeling fundamentals.

• Basic understanding of authentication/authorization patterns and web application security.

• Awareness of distributed systems concepts (load balancing, caching, message queues).

AI/ML Foundations

• Completed coursework or projects involving machine learning, NLP, or deep learning.