Software Engineer (NodeJS, AI-native)

The Position

MDaaS is OPSWAT's cloud-native engine framework, built on Kubernetes microservices and handling massive real-time file traffic for customers worldwide. We're looking for an AI-native Software Engineer to join our growing R&D Product Engineering Team and help build the next generation of security products with AI woven into every step of how you design, code, and ship.

What You Will Be Doing

• Build and ship production services the AI-native way pair with agentic coding tools (Claude Code, GitHub Copilot, Cursor) as a first-class part of your daily workflow to design, implement, and refactor back-end services at higher velocity and quality.
• Build and operate the team's AI SDLC: set up and maintain the AI-assisted development pipeline: agent configurations, prompt/context templates (e.g. CLAUDE.md, project rules), MCP integrations, and guardrails so the whole team can code, review, and ship reliably with AI in the loop.
• Relentlessly pursues quality through multiple levels of automated tests, including but not limited to unit, API, End to End, and load in cloud managed services environment, augmenting coverage with AI-assisted test generation and review.
• Work within an agile scrum team, contributing to an atmosphere of continuous improvement.
• Document, collaborate and communicates issues effectively found during the course of development and testing and works to resolve the issue.
• Document software changes and AI workflows/prompts for use by other engineers, quality assurance and documentation specialists.
• Master the technologies, languages, and practices used by the team and project assigned including the AI tooling that accelerates them.
• Develop infrastructure as code to reliably deploy applications on demand or through automation.

What We Need From You

• BA/BS in Computer Science, Technology or a related field or equivalent work experience.

Experience:

• 2–4 years of professional software engineering experience.
• Hands-on experience shipping real code with AI-assisted / agentic development tools (Claude Code, GitHub Copilot, Cursor, or similar).

Soft-skills:

• Excellent verbal and written communication skills.
• Self-motivated with a proven ability to work well in a fast-paced team environment.
• Ability to learn new development languages and new AI tools/models quickly and apply that knowledge effectively.
• Passionate about solving problems in an elegant and principled manner.
• Enthusiast about team work, learning and teaching including sharing AI workflows and best practices with the team.
• Ability to communicate and clarify requests, and to write clear, effective instructions/prompts for AI agents.

Skills/Knowledge:

• Relevant experience in back-end development with JavaScript/Typescript (NodeJS) and/or Golang.
• Experience with NodeJS Design Patterns and best practices.
• Experience with logging/tracing platform such as Kibana, Datadog.
• Experience with SQL or NoSQL databases like PostgreSQL or MongoDB.
• Experience with AWS or any other cloud provider.
• Experience building or operating a large scale Cloud service/ micro-service architecture.
• Experience working with Linux, K8S, and Docker.
• Able to knowledgeably discuss performance, security, and user interactions in complex systems.
• Knowledgeable about algorithms and data structures.
• Practical experience with AI SDLC (AI-assisted Software Development Life Cycle): using AI coding tools day-to-day and knowing how to review, validate, and take responsibility for AI-generated code.

It Would Be Nice If You Had

• Experience in Cybersecurity Industry.
• Experience with protecting information in compliance with NIST, HIPAA etc.
• Able to design and operate AI workflows: configuring agentic coding tools (Claude Code, MCP servers, custom agents/skills), building AI pipelines with frameworks like LangChain or AutoGen, or integrating AI into CI/CD and the development pipeline.
• Awareness of AI engineering fundamentals: prompt/context engineering, evaluation of model output, and cost/latency/quality trade-offs across models.