AI Software Engineer
DNV · Chennai
The AI Software Engineer is responsible for developing production-grade AI-enabled platform capabilities, APIs, services, and reusable enhancements across Digital & Transformation. This role sits within the Data & AI Engineering section and contributes to the development of scalable capabilities that can be reused across multiple platform implementations.
This role sits within Digital & Transformation, helping to advance how DNV performs Due Diligence, Verification & Assurance, and Renewables Certification work across Energy Systems.
Working in close partnership with Product leadership, Solution Engineering, Application Engineering, Platform Reliability, and Solution Architecture, this role translates product needs, implementation feedback, and complex workflow requirements into reliable, maintainable, and production-ready AI-enabled software.
The AI Software Engineer builds capabilities that support complex AI scenarios involving model integration, prompt engineering, retrieval, advanced extraction logic, agent configurations, APIs, and reusable platform enhancements. The role also supports API-enabled services and integration patterns that allow AI capabilities to connect effectively with web applications, data services, workflow configurations, and broader platform components.
This role requires strong software engineering fundamentals, practical experience with AI-enabled development, and the ability to build secure, scalable systems that support customer-facing platform delivery. Python, JavaScript, and Node.js are primary technologies for this role, with UI framework experience considered helpful but not required for every hire.
**This role is based at our DNV office in Chennai, India. Further details regarding role-specific requirements will be shared during the interview process.**
Key Responsibilities
AI Engineering & Delivery
- Design, develop, test, and maintain AI-enabled platform capabilities, APIs, services, and reusable software enhancements.
- Translate product requirements, implementation feedback, and technical design direction into scalable, maintainable, and production-ready systems.
- Review feature requests from Solution Engineering and support development of reusable platform capabilities where requirements should become product or platform enhancements.
- Build capabilities that can be leveraged across multiple customer implementations, workflows, and platform experiences.
- Write clean, well-structured, testable, and maintainable code that supports long-term platform quality and team velocity.
Modern Development & AI-Enabled Engineering
- Use AI-assisted development practices to accelerate coding, testing, documentation, refactoring, and engineering analysis.
- Apply LLM-enabled workflows responsibly while maintaining strong code quality, security, review practices, and maintainability.
- Contribute to reusable engineering patterns, documentation, and development practices that improve team velocity and consistency.
- Use AI-enabled tools where appropriate to support test generation, code review preparation, technical documentation, and developer productivity.
Architecture, Platforms & Interoperability
- Build APIs, services, prompt patterns, agent integrations, extraction workflows, and integration patterns that support platform interoperability.
- Develop backend services and API-enabled capabilities using technologies such as Python, JavaScript, Node.js, and related frameworks.
- Collaborate with Application Engineering, Solution Engineering, Platform Reliability, and Solution Architecture to align technical decisions across the platform.
- Promote reusable components, shared services, consistent data contracts, and integration standards.
- Support the appropriate balance between low-code platform implementation, custom engineering, reusable AI capabilities, and API-enabled services.
Data Science & AI Integration
- Support model integration, prompt engineering, retrieval patterns, advanced extraction logic, AI quality measurement, and agent configuration.
- Ensure AI-driven features are production-ready, scalable, cost-effective, secure, and aligned with platform needs.
- Partner with Solution Engineering on complex document types, failed extractions, prompt optimization, reusable implementation patterns, and AI quality improvements.
- Help package mature AI capabilities, prompts, evaluation methods, and configuration guidance so they can be reused across customer implementations.
- Collaborate with Data & AI Engineering peers to ensure AI outputs, extracted data, and model-driven capabilities are reliable, testable, traceable, and understandable to users.
DevOps, Reliability & Security
- Contribute to CI/CD pipelines, automated testing, deployment automation, monitoring, and incident readiness.
- Build software with strong attention to reliability, performance, availability, scalability, and operational support.
- Support observability practices including logging, monitoring, error handling, and production diagnostics.
- Follow secure development practices aligned with internal security standards, SOC 2 Type II, ISO 27001, and enterprise security expectations.
- Ensure AI-enabled capabilities are developed with appropriate attention to data handling, access control, auditability, and responsible AI practices.
Collaboration & Technical Ownership
- Work effectively with Product, Solution Engineering, Application Engineering, Platform Reliability, Solution Architecture, and cross-functional stakeholders.
- Take ownership of assigned features, services, defects, and technical improvements from design through production support.
- Communicate technical tradeoffs clearly and contribute to pragmatic engineering decisions.
- Support knowledge transfer to Solution Engineering and Application Engineering where AI capabilities become reusable implementation or application patterns.
- Contribute to a culture of accountability, collaboration, continuous improvement, and delivery excellence.