Job Description
Shape the Future of AI in 2026
Are you a visionary technical leader ready to define the technological landscape of 2026? FutureScale Technologies is seeking a Senior AI Architect to spearhead our next-generation AI infrastructure. In this pivotal role, you will architect scalable machine learning systems that power our enterprise clients into the future.
We are not just building software; we are defining the strategic roadmap for the next era of technology. If you are passionate about Generative AI, Large Language Models (LLMs), and ethical AI deployment, we want to hear from you.
Why Join Us?
As a leader in the tech sector, we offer a competitive package, equity opportunities, and the chance to work on cutting-edge projects that will define the industry in 2026 and beyond.
Responsibilities
- Architect End-to-End Pipelines: Design and implement scalable machine learning architectures and data pipelines tailored for 2026-scale data volumes.
- Lead GenAI Innovation: Spearhead the research and development of Generative AI applications, integrating LLMs into core product workflows.
- Optimize Performance: Monitor, tune, and optimize model performance in production environments to ensure low latency and high accuracy.
- Mentorship & Strategy: Guide a team of junior data scientists and engineers, fostering a culture of continuous learning and technical excellence.
- Collaborate with Product: Work closely with cross-functional product teams to translate business requirements into technical AI solutions.
- Cloud Integration: Manage infrastructure on AWS or Azure, ensuring robust security and scalability for AI workloads.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Mathematics, Statistics, or a related technical field.
- Experience: 5+ years of experience in software engineering or machine learning, with at least 2 years in a senior or lead architectural role.
- Technical Stack: Deep proficiency in Python, PyTorch, TensorFlow, or JAX.
- LLM Expertise: Hands-on experience with LLMs, fine-tuning, RAG (Retrieval-Augmented Generation), and prompt engineering.
- Cloud Mastery: Strong understanding of cloud-native architecture (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Problem Solving: Proven ability to solve complex, ambiguous problems with elegant, scalable technical solutions.