AI Engineer

At DOF Robotics, we create unforgettable experiences to entertain people by engineering their emotions and using special effects. As dream weavers, we create immersive worlds of storytelling and story-living experiences. Strong storytelling experience enforced with mechanical engineering, automation engineering, software development, and high-end movie content lets people dive deep into a story and experience feelings they have never lived before.  

DOF means degrees of freedom, depth of feeling, and design of the future. Adrenaline is in our veins, passion is in our hearts, and stories are in our dreams.

If you want to be a part of this storytelling and build your career in the entertainment world, it is your turn. Become a dream weaver and change the destiny of many. 

JOB BRIEF

We are currently hiring an “AI Engineer” to design, develop and deploy artificial intelligence solutions that drive innovation and efficiency.

KEY REQUIREMENTS

• 3+ years of experience in AI model development and optimization,
• Proficiency in Python, PyTorch, TensorFlow, or JAX for AI model development. Deep expertise in LLMs (Large Language Models), NLP, and image processing,
• Proficiency in Python, PyTorch, TensorFlow, or JAX for AI model development,
• Strong knowledge of Deepseek, LLaMA, and Qwen for offline LLM optimization and deployment,
• Hands-on experience with LoRA and QLoRA for model optimization,
• Expertise in InstantID, Whisper, OpenCV, and face recognition systems,
• Ability to build custom local vector databases instead of relying on third-party solutions,
• Experience with Docker, Kubernetes, and REST API integration,
• Strong understanding of TCP, IP, UDP, and HTTP protocols and API development for offline AI systems,
• Analytical thinking and problem-solving skills,
• Excellent time management skills with the ability to manage multiple priorities and deadlines,
• Disciplined for follow-up; ability to perform and prioritize multiple tasks seamlessly with attention to details.
• Developing AI agents on automation platforms like N8N, creating and managing workflows such as automatic email, call and meeting scheduling.

JOB DESCRIPTION

• Working with Large Language Models (LLM),
• Deploy, optimize, and fine-tune offline Large Language Models (LLMs) such as Deepseek, LLaMA, and Qwen,
• Utilize LoRA and QLoRA techniques to reduce model size and optimize performance,
• Develop Retrieval-Augmented Generation (RAG) systems integrated with AI models,
• Perform data preprocessing and model training to enhance AI performance for specific tasks,
• Build and optimize a custom offline local vector database, instead of using solutions like Pinecone, Weaviate, or FAISS,
• Integrate this database with LLMs to improve knowledge retrieval,
• Design data indexing and search algorithms to enhance the efficiency of offline AI knowledge retrieval,
• Develop APIs to ensure seamless communication between LLMs, STT/TTS, and face recognition AI systems,
• Have a strong command of TCP, IP, UDP, and HTTP protocols to enable low-latency, reliable data exchange in offline AI systems,
• Build an optimized data-sharing infrastructure between different AI components,
• Train AI models compatible with InstantID and improve face recognition performance,
• Utilize techniques such as Self-Supervised Learning, Contrastive Learning, and Federated Learning to enhance face aging and recognition algorithms,
• Deploy and optimize Whisper models for offline use,
• Develop low-latency speech processing solutions for AI systems to enable real-time voice interaction,
• Optimize models beyond fine-tuning using techniques like Transfer Learning, Continual Learning, and Reinforcement Learning,
• Improve AI model efficiency by reducing computational resource usage through quantization, pruning, and distillation,
• Collaborate with data engineers to build robust AI data pipelines,
• Develop custom AI deployment solutions for fully offline AI systems,
• Deploy AI models using Docker & Kubernetes on local infrastructures,
• Optimize offline AI models for efficient hardware utilization.