Backend Engineer

Company Overview

Established in 2010, Vinova is an award-winning development company specializing in mobile, web, and enterprise applications. Serving global clients across IoT, blockchain, fintech, and ecommerce, Vinova delivers quality products through passion, collaboration, and cutting-edge technology.

Job Summary

As a Backend Engineer at Vinova, you will design, build, deploy, and operate secure backend services that power AI, machine learning, and generative AI solutions for government systems and digital services. Collaborate across teams to deliver reliable, production-ready systems.

Responsibilities

Design, build, deploy, and operate secure backend services to enable AI, machine learning, and generative AI solutions for government and digital services

Collaborate with product, policy, data, cybersecurity, DevOps, and platform teams to translate operational needs into reliable, production-ready backend systems

Develop and maintain APIs, integrations, and application components to support AI-enabled products and platforms

Apply strong software engineering fundamentals and backend development practices to enhance AI and machine learning solutions

Implement data preparation, model evaluation, monitoring, logging, and production support processes for AI systems

Utilize cloud environments such as AWS, GCC, or approved enterprise cloud platforms to deploy and manage backend services

Incorporate responsible AI principles, AI governance, data protection, security, privacy, and risk management into backend engineering practices

Employ CI/CD, DevSecOps, version control, testing, and operational support processes to ensure system reliability and security

Preferred competencies and qualifications

Degree in Computer Science, Software Engineering, or a relevant IT field

1-2 years of experience in AI engineering, machine learning engineering, data science, software engineering, or related technical roles

Hands-on experience developing backend services, APIs, integrations, or application components

Strong programming experience in Python

Familiarity with API development, testing, and commonly used AI/machine learning libraries or frameworks

Experience with large language models, prompt engineering, embeddings, vector databases, retrieval-augmented generation, or AI agent workflows

Good understanding of MLOps/LLMOps practices including monitoring, logging, and production support