Job Description
Are you ready to define the technology landscape of 2026? Nexus Future Systems is seeking a visionary Senior AI & Machine Learning Architect to lead our research and engineering initiatives. We are building the infrastructure that will power the next decade of digital transformation, focusing on autonomous systems, generative AI, and scalable cloud ecosystems.
In this pivotal role, you will bridge the gap between theoretical research and production-grade deployment, ensuring our solutions are not just functional today, but future-proof for the era of 2026 and beyond.
Responsibilities
- Lead the architectural design of high-performance machine learning pipelines capable of handling real-time, exabyte-scale data streams.
- Drive R&D initiatives for emerging AI paradigms, specifically focusing on Large Language Models (LLMs) and generative adversarial networks (GANs).
- Optimize model efficiency to ensure low-latency inference on edge devices and distributed cloud environments.
- Collaborate cross-functionally with product managers and data scientists to translate business requirements into robust technical roadmaps.
- Establish best practices for code review, testing, and deployment within a high-velocity Agile environment.
- Mentor senior engineering teams on advanced neural network architectures and ethical AI implementation.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, or a related technical field.
- Experience: Minimum of 7+ years of experience in software engineering with a focus on machine learning and AI.
- Technical Skills: Deep expertise in Python, PyTorch, TensorFlow, or JAX. Proven track record deploying models at scale.
- Cloud Mastery: Strong proficiency in AWS, GCP, or Azure with a focus on MLOps and serverless architectures.
- System Design: Ability to design fault-tolerant, scalable distributed systems using microservices and containerization (Kubernetes/Docker).
- Problem Solving: Exceptional analytical skills with a passion for solving complex, unstructured problems.