Junior AI Native Engineer

About Cerebra

Cerebra specializes in delivering top-tier talent at competitive rates by employing highly skilled professionals drawn from leading consulting firms. The company prioritizes quality over rapid growth to ensure successful outcomes for its clients.

Role Overview

This role involves developing as a next-generation AI-native engineer who integrates AI deeply into the software engineering workflow. As a Software AI Engineer, you will design, develop, and deploy production-grade full-stack applications leveraging AI-assisted tools as everyday essentials. The position offers exposure to diverse industries and enterprise technology stacks, with unique access to partnerships with top AI vendors and structured certification opportunities.

Key Responsibilities

• Regularly utilize AI coding assistants to enhance productivity and code quality during software delivery.
• Implement Large Language Model (LLM) APIs within live applications, handling token management, latency, and abstraction layers.
• Integrate AI throughout the software lifecycle, including AI-generated testing, debugging assistance, code review acceleration, and prompt engineering.
• Ensure the reliability and quality of AI-generated outputs by applying sound engineering judgment on their readiness and limitations.
• Establish and monitor KPIs to measure the impact and return on investment of AI-assisted development workflows, and communicate these metrics to stakeholders.
• Manage the full delivery process end-to-end within Agile sprint cycles in collaboration with client engineering teams.
• Contribute to communal knowledge bases, develop reusable components, and help define internal AI tooling standards.
• Develop and connect application layers, APIs, and interfaces that link full-stack systems with agent-based AI backends, managing data flow and integration points.

Required Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or related disciplines.
• Minimum two years of software engineering experience in production settings, or equivalent practical experience evidenced via projects or internships.
• Proficiency in at least one backend programming language such as Python, Java, or TypeScript.
• Hands-on experience actively applying AI tools in engineering tasks, including real-world usage of LLM APIs with an understanding of operational considerations like token limits, latency, and cost.
• Basic knowledge of web technologies including JavaScript, HTML, and CSS.
• Familiarity with cloud platforms (AWS, Azure, or GCP), containerization (Docker), and continuous integration/delivery pipelines.
• Understanding of Agile development methodologies.
• Experience working with SQL or NoSQL databases.
• Capability to assess and enhance AI-generated outputs responsibly, acknowledging AI constraints.
• Awareness of agentic systems concepts including orchestration frameworks such as LangChain or LangGraph, retrieval-augmented generation (RAG) pipelines, and their integration with full-stack applications. Practical experience in production preferred; conceptual knowledge required.

Additional Information

This opportunity provides exceptional breadth across industries and technology stacks, coupled with access to leading AI vendor collaborations and structured pathways for career development toward advanced roles such as Forward Deployed Engineer.