Job Description
We are building the next generation of cognitive infrastructure. FutureScale AI is seeking a visionary Senior AI/LLM Engineer to lead the development of proprietary Large Language Models designed to redefine human-machine interaction in 2026 and beyond.
In this role, you will not just write code; you will architect the future of intelligence. You will work in a high-performance environment, pushing the boundaries of generative AI, fine-tuning, and retrieval-augmented generation (RAG).
Why Join Us?
- Work on cutting-edge AI infrastructure.
- Competitive compensation and equity package.
- Remote-first culture with flexible working hours.
If you are passionate about the future of Artificial Intelligence and possess the technical prowess to execute, we want to hear from you.
Responsibilities
- Architect Scalable LLM Systems: Design and implement robust pipelines for training, fine-tuning, and deploying large language models at scale.
- Model Optimization: Reduce latency and optimize inference costs while maintaining high model accuracy and safety standards.
- RAG Strategy: Lead the integration and optimization of Retrieval-Augmented Generation architectures to enhance factual correctness.
- Research & Innovation: Stay ahead of the curve with the latest advancements in NLP, Transformer architectures, and multimodal AI.
- Collaboration: Partner with product managers and data scientists to translate business requirements into technical AI solutions.
- Performance Monitoring: Establish robust monitoring and evaluation frameworks to continuously assess model performance.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related technical field.
- Experience: 5+ years of professional experience in machine learning, NLP, or deep learning engineering.
- Technical Stack: Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- LLM Expertise: Deep understanding of Transformer models, Hugging Face Transformers, and fine-tuning methodologies (LoRA, QLoRA).
- MLOps: Experience with cloud platforms (AWS/GCP/Azure) and containerization tools (Docker, Kubernetes).
- Problem Solving: Proven track record of solving complex technical challenges in high-scale environments.