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.
- Flexible work arrangements for better work-life balance
- Generous Paid Leaves (Annual, Sick, Compassionate, Local Public, Marriage, Maternity, Paternity, Medical leave)
- Medical benefits ( Insurance and Annual Health Check-up)
- Pension and Insurance Policies (Group Term Life Insurance, Group Personal Accident Insurance, Travel Insurance)
- Training and Development Assistance (Training Sponsorship, On-The-Job Training, Training Programme)
- Additional Benefits (Long Service Awards, Mobile Phone Reimbursement)
- Company bonus/Profit share.
*Benefits may vary based on position, tenure/contract/grade level*
DNV is an Equal Opportunity Employer and gives consideration for employment to qualified applicants without regard to gender, religion, race, national or ethnic origin, cultural background, social group, disability, sexual orientation, gender identity, marital status, age or political opinion. Diversity is fundamental to our culture and we invite you to be part of this diversity.
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Information Systems, or related field, or equivalent experience.
- 3+ years of experience developing production software, APIs, services, backend capabilities, or AI-enabled platform features.
- Strong programming skills in Python, JavaScript, and Node.js.
- Experience building APIs, backend services, integrations, or reusable software capabilities in production environments.
- Experience with LLMs, prompt engineering, model integration, retrieval, extraction workflows, evaluation methods, or agentic patterns.
- Experience with automated testing, CI/CD, source control, code review, and modern software delivery practices.
- Strong understanding of software design, testing, code quality, maintainability, security, and performance.
- Ability to collaborate with Product, Solution Engineering, Application Engineering, Platform Reliability, Solution Architecture, and cross-functional stakeholders.
- Strong problem-solving skills and attention to software quality, security, maintainability, and production readiness.
What is Preferred
- Experience in product-led or platform-based organizations.
- Experience with cloud-native application development, deployment, or support in Azure, AWS, or comparable cloud environments.
- Familiarity with low-code or hybrid development environments.
- Experience supporting interoperability across multiple systems or enterprise platforms.
- Experience with document processing, structured extraction, retrieval-augmented generation, evaluation frameworks, or AI quality measurement.
- Experience with modern UI frameworks such as Vue.js, React, or comparable frontend frameworks.
- Background in energy, infrastructure, renewables, assurance, certification, or other technically complex industries.
- Experience contributing to reusable technical standards, implementation patterns, prompt libraries, or shared platform capabilities.
Security and compliance with statutory requirements in the countries in which we operate is essential for DNV. Background checks will be conducted on all final candidates as part of the offer process, in accordance with applicable country-specific laws and practices.