Company
Technology
AIEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“AI Engineer. Skills: LLMs, Structured generation, Fine-tuning, Production AI. Design fine-tuning strategies. Build supervised datasets”
What You'll Achieve.
improve generation quality, reliability, efficiency, and usability; improve model reliability and output quality; shape production-grade AI systems
Industry & Context.
model failure analysis; debugging systems; retry, repair, and self-correction workflows
What They're Looking For.
Must Have
experience building with LLMs or structured generation systems in production or applied research settings, Hands-on experience fine-tuning or adapting open-source language models, Python engineering skills, Experience building evaluation systems, ML experimentation workflows, or data pipelines, understanding of prompt engineering, structured outputs, tool use, and model failure analysis, Ability to define measurable evaluation criteria rather than relying only on subjective review, Comfort debugging systems spanning model outputs, validation systems, runtime behavior, and rendered results, communication and collaboration skills
Nice to Have
Experience with code generation, DSL generation, or compiler-aware AI systems, Experience with LoRA, QLoRA, SFT, preference tuning, distillation, or synthetic data generation, Familiarity with animation systems, graphics pipelines, design tools, SVG, WebGL, shaders, or procedural graphics, Experience with multimodal or visual-language-model evaluation workflows, Experience with observability or ML evaluation tooling such as Weights & Biases, Langfuse, MLflow, or OpenTelemetry, Experience building agentic systems, orchestration pipelines, or multi-step generation workflows, Familiarity with ASTs, intermediate representations (IRs), or structured program representations
What You'll Do.
Design fine-tuning strategies
Build supervised datasets
Develop measurable benchmarks
Experiment with open-source models
Implement fine-tuning approaches
Build automated data pipelines
Use validation systems for feedback
Improve retry workflows
Collaborate cross-functionally
How You'll Work.
Team & Collaboration
Collaborate cross-functionally with engineering, design, and product teams
Communication Scope
communication and collaboration skills
Full Job Description
- Fully remote position - Candidates should have at least 4 hours of overlap with the Malaysia time zone (MYT / UTC+8) - Direct hire opportunity - Visa sponsorship is not available for this role ABOUT THE COMPANY We are partnering with a fast-growing technology company building next-generation AI-powered creative tools for animation, design, and interactive media workflows. Their platform is used to help creators and teams accelerate visual content production through intelligent generation, editing, and automation systems. The company operates at the intersection of generative AI, structured content systems, and creative tooling, with a strong focus on production-grade AI infrastructure, model reliability, and real-world usability. Their engineering culture emphasizes experimentation, measurable quality improvements, and scalable AI systems deployed directly into user-facing products. ABOUT THE ROLE We’re looking for an AI Engineer to help build specialized generation, editing, evaluation, and optimization systems for creative and structured content workflows. This role focuses on structured generation, domain-specific model adaptation, evaluation systems, feedback pipelines, and production AI infrastructure. You’ll work closely with engineering, design, and product teams to improve generation quality, reliability, efficiency, and usability across AI-assisted creative workflows. This is an opportunity to work on highly applied AI problems involving: - Structured and constrained generation - AI-assisted editing systems - Evaluation and observability pipelines - Fine-tuning and adaptation of open-source models - Multi-step generation and repair workflows - Scalable production AI systems WHAT YOU’LL WORK ON - Natural-language-to-structured-content generation workflows - Structure-preserving editing and modification systems - Validation and repair pipelines for generated outputs - Evaluation systems for quality, correctness, consistency, and runtime performance - Traini
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