Job Description
We are not just building software for today; we are architecting the infrastructure for 2026 and beyond. At FutureScale Technologies, we are pioneering the next generation of Generative AI and predictive modeling. We are looking for a visionary Senior AI Infrastructure Engineer to lead our technical roadmap, ensuring our systems are scalable, secure, and future-proof.
In this pivotal role, you will bridge the gap between cutting-edge research and production-grade engineering. You will define the architecture for our core AI engines, optimize deep learning pipelines, and mentor a team of high-performing engineers. If you are passionate about the future of technology and want to shape the digital landscape of 2026, we want to hear from you.
Why Join Us?
- Impactful Work: You will build the backbone of our AI ecosystem.
- Future-Ready Stack: Work with the latest in Rust, Kubernetes, and GPU acceleration.
- Competitive Compensation: Top-tier salary and equity package.
Responsibilities
- Design and implement scalable, high-performance AI infrastructure architectures capable of handling petabyte-scale data.
- Optimize machine learning models for low-latency inference and high-throughput training runs.
- Lead the migration of legacy systems to modern, cloud-native microservices using Kubernetes and Docker.
- Establish best practices for code quality, security (DevSecOps), and observability within the AI team.
- Collaborate with research scientists to translate theoretical models into deployable, production-grade software.
- Define the technical roadmap for the 2026 release cycle, including the integration of next-gen hardware accelerators.
- Mentor and develop junior engineers, conducting code reviews and technical training sessions.
Qualifications
- Master’s degree or PhD in Computer Science, Machine Learning, or a related technical field.
- 8+ years of experience in software engineering, with at least 3 years specifically focused on AI/ML infrastructure.
- Deep expertise in Python, PyTorch, TensorFlow, or similar deep learning frameworks.
- Strong proficiency in cloud platforms (AWS, GCP, or Azure) and container orchestration (Kubernetes).
- Experience with distributed systems, message queues (Kafka, RabbitMQ), and high-performance computing (HPC).
- Familiarity with MLOps tools (MLflow, Kubeflow) and CI/CD pipelines.
- Strong problem-solving skills and the ability to thrive in a fast-paced, ambiguous startup environment.