Job Description
Shape the Future of Intelligence.
We are looking for a visionary Senior AI Architect to lead the development of next-generation generative models for the 2026 era. At FutureScale Technologies, we are building the infrastructure that will power the autonomous economy of tomorrow. If you are obsessed with scaling LLMs, optimizing inference, and ensuring ethical AI deployment, we want to hear from you.
In this role, you will bridge the gap between theoretical machine learning research and production-grade engineering, ensuring our systems are robust, scalable, and ready for the exponential growth expected in 2026.
Responsibilities
- Architect Scalable MLOps Pipelines: Design and maintain end-to-end infrastructure for training and deploying large-scale generative AI models.
- Model Optimization: Implement quantization, pruning, and distillation techniques to reduce latency and cost without sacrificing performance.
- Real-time Inference Systems: Build high-throughput, low-latency APIs to serve AI models to millions of concurrent users.
- RAG & Knowledge Graph Integration: Develop advanced Retrieval-Augmented Generation systems to enhance model accuracy and reduce hallucinations.
- AI Safety & Compliance: Implement guardrails and ethical frameworks to ensure AI outputs align with safety guidelines and regulatory standards.
- Performance Monitoring: Establish comprehensive observability stacks to track model drift, system health, and user engagement metrics.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, Mathematics, or a related technical field.
- Experience: 5+ years of experience in Machine Learning Engineering, with at least 2 years specifically focused on Large Language Models (LLMs) or Generative AI.
- Programming: Proficiency in Python, PyTorch, TensorFlow, and experience with Rust or Go for high-performance backend services.
- Cloud Expertise: Deep understanding of cloud architecture (AWS/GCP/Azure) and serverless computing.
- Infrastructure: Proven experience with Kubernetes, Docker, and distributed systems.
- Problem Solving: Ability to troubleshoot complex, multi-dimensional technical challenges in a fast-paced environment.