Development Engineer-GenAI

Role & responsibilities

• Design, build, and deploy AI/GenAI-based applications that serve Digital Twin use cases, using Angular, Node.js, and TypeScript.
• Work hands-on with LLMs and GenAI APIs (e.g., OpenAI, Anthropic, Azure OpenAI) to implement features like smart assistants, summarization, chat interfaces, decision support, and insight extraction.
• Develop and iterate on prompt engineering strategies, including prompt chaining, dynamic context building, function calling, and few-shot/zero-shot setups.
• Own the full GenAI lifecycle: data preparation, API integration, system design, prompt tuning, validation, testing, deployment, and monitoring.
• Optimize AI responses for accuracy, reliability, latency, cost, and relevance.
• Collaborate closely with product managers, QA engineers, and AI researchers to translate business problems into AI solutions that feel natural and useful.
• Write clean, testable, modular, and maintainable code, integrating GenAI services with your UI and backend layers.
• Build guardrails and fallback mechanisms to ensure responsible and safe AI output in production.
• Take ownership of code quality and maintainability; contribute to internal standards and reviews.
• Demonstrate a never-give-up mindset, a delivery-first attitude, and a commitment to continuous learning in the evolving GenAI landscape.
• Write clean, modular, well-tested code using Angular, Node.js, and TypeScript.
• Perform unit testing, code reviews, and continuous integration/deployment practices.
• Troubleshoot, debug, and optimize existing applications for performance and scalability.

Preferred candidate profile

• 5+ years of software development experience, with strong hands-on skills in Angular, Node.js, and TypeScript.
• 3+ years working directly with LLMs and GenAI platforms, building and deploying production-grade features.
• Proven expertise in prompt engineering and understanding of LLM behaviours (hallucinations, context length, token usage, determinism).
• Experience with GenAI workflows, including RAG (Retrieval-Augmented Generation), tool/function calling, embeddings, and system prompts.
• Ability to debug, test, and evaluate AI outputs for correctness, bias, and performance.
• Strong understanding of software development principles, clean architecture, and modular design.
• Familiarity with RESTful APIs, microservices architecture, and cloud environments (e.g., AWS, Azure, GCP