AI Development Engineer - Backend

An AI Development Engineer is responsible for solving complex engineering problems by using AI to improve speed, quality, and cost efficiency. This role reimagines traditional software engineering for an AI-augmented world moving from coding-heavy execution to system orchestration, structured thinking, validation, and agentic application development. The ideal candidate is a strong hands-on engineer who can work with AI tools, foundation models, multimodal systems, and research-driven implementation to build reliable, scalable, production-ready AI applications.

Responsibilities:

• Translate ambiguous product or business requirements into clear technical specifications, system designs, and validation plans.
• Build, maintain, and scale AI-powered production systems and agentic applications.
• Design workflows where LLMs, VLMs, tools, APIs, retrieval systems, and traditional microservices work together reliably.
• Use AI-assisted development tools while maintaining code quality, security, performance, and long-term maintainability.
• Prevent AI-generated code bloat, dead code, and unnecessary complexity.
• Build observability, evaluation, testing, and monitoring systems for AI-generated outputs.
• Optimize prompts, model selection, workflows, and inference cost.
• Apply Responsible AI principles, including safety, privacy, fairness, transparency, and accountability.
• Work with cross-functional teams across product, engineering, security, data, and design.
• Mentor engineers to move from implementation-heavy development to system orchestration and AI-first engineering practices.

Requirements:

• Strong software engineering fundamentals and hands-on experience building production-grade systems.
• Experience in one or more modalities such as: Vision/images, video, text, audio, speech.
• Deeper knowledge and hands-on experience with LLMs, VLMs, multimodal AI systems, and foundation model architectures.
• Ability to actively find, read, understand, and implement ideas from AI research papers.
• Experience building or integrating agentic applications, AI copilots, RAG systems, workflow automation, or multimodal GenAI systems.
• Strong understanding of system design, APIs, data flows, testing, observability, security, and production reliability.
• Practical experience with prompt engineering, context engineering, model evaluation, benchmarking, and AI workflow orchestration.
• Ability to convert ambiguous requirements into structured specs, implementation plans, and validation frameworks.
• Strong debugging, problem-solving, communication, ownership, and learning agility.
• NOTE: This is a 6-month contractual role, with the possibility of extension based on performance and business requirements.