Job Description
Are you ready to architect the technological landscape of 2026? Nexus Future Labs is seeking a visionary AI/ML Infrastructure Architect to lead the deployment of next-generation artificial intelligence systems. In this pivotal role, you will bridge the gap between cutting-edge research and scalable production engineering, ensuring our platforms are robust, efficient, and ready for the future.
We are looking for a thought leader who doesn't just keep up with trends but defines them. You will be responsible for the architecture of our neural network pipelines, optimizing for both computational efficiency and massive scale. Join us in building the intelligent core of tomorrow’s digital ecosystem.
Why join us?
- Work on projects that are shaping the trajectory of AI development.
- Competitive compensation package with equity options.
- Flexible remote-first culture with state-of-the-art equipment.
- Access to proprietary datasets and computing resources.
Responsibilities
- Architect Advanced Pipelines: Design and implement scalable infrastructure for training and deploying large-scale Machine Learning models and Generative AI applications.
- Optimize Performance: Drive technical excellence in inference latency, throughput, and model optimization strategies to reduce cloud costs.
- System Scalability: Lead the migration and management of distributed systems, ensuring 99.99% uptime and high availability.
- Future-Proofing: Evaluate emerging technologies (e.g., Quantum-ready architectures, Edge AI) and integrate them into our roadmap.
- Team Leadership: Mentor junior engineers and data scientists, fostering a culture of innovation and technical rigor.
- Security & Compliance: Implement rigorous security protocols and data governance frameworks compliant with future regulatory standards.
Qualifications
- Education: Master’s degree in Computer Science, Artificial Intelligence, or a related technical field (PhD preferred).
- Experience: 5+ years of experience in software engineering, with at least 3 years specifically in AI/ML infrastructure.
- Technical Stack: Deep proficiency in Python, PyTorch, TensorFlow, and SQL.
- Cloud Mastery: Extensive experience with cloud providers (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker).
- Architecture: Strong understanding of distributed systems, microservices, and MLOps practices.
- Problem Solving: Proven ability to solve complex engineering challenges under tight deadlines.