Job Description
Nebula Core Systems is at the forefront of the technological revolution, building the intelligent infrastructure that will power the digital landscape of 2026. We are looking for a visionary Senior AI Architect to design scalable, robust, and ethical AI systems. In this role, you will not just use existing tools; you will help define the architectural standards for the next generation of machine learning, focusing on generative AI, edge computing, and real-time inference.
If you are passionate about the future of technology and want to lead a team that pushes the boundaries of what's possible, we want to hear from you.
Why Join Us?
- Work on high-impact projects that define the future of enterprise AI.
- Competitive compensation package including equity and performance bonuses.
- Flexible remote-first work culture with premium health benefits.
- Access to cutting-edge hardware and research facilities.
Responsibilities
- Architect Scalable Solutions: Design and implement end-to-end AI architectures capable of handling petabyte-scale data streams with low latency.
- Lead Model Development: Spearhead the research and development of advanced Deep Learning models, focusing on LLMs and reinforcement learning agents.
- MLOps Implementation: Build and maintain robust MLOps pipelines to automate model training, testing, and deployment, ensuring continuous integration and delivery.
- Cross-Functional Collaboration: Partner with product managers, data scientists, and engineers to translate complex business requirements into technical AI roadmaps.
- Ethical AI Governance: Establish frameworks for bias detection, data privacy, and algorithmic transparency to ensure responsible AI deployment.
- Technical Mentorship: Guide and mentor junior architects and engineers, fostering a culture of innovation and continuous learning.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related technical field (or equivalent extensive industry experience).
- Experience: 7+ years of professional experience in software engineering, machine learning, or artificial intelligence.
- Technical Proficiency: Deep expertise in Python, PyTorch, TensorFlow, and modern deep learning frameworks.
- Infrastructure: Strong background in distributed systems, cloud platforms (AWS/GCP), containerization (Docker/Kubernetes), and serverless architecture.
- MLOps: Hands-on experience with MLOps tools such as MLflow, Airflow, and SageMaker.
- Problem Solving: Proven ability to troubleshoot complex system issues and optimize performance under heavy load.
- Communication: Exceptional verbal and written communication skills, with the ability to articulate technical concepts to diverse stakeholders.