Job Description
We are on a mission to engineer the future of intelligent systems. Nexus Future Labs is seeking a visionary Senior AI Solutions Architect to design and implement scalable, next-generation artificial intelligence architectures for 2026 and beyond.
In this pivotal role, you will bridge the gap between cutting-edge machine learning research and robust, production-ready engineering. You will lead a team of talented engineers, define technical roadmaps, and ensure our AI solutions are secure, ethical, and transformative.
Why Join Us?
- Work on groundbreaking AI projects that define industry standards.
- Competitive compensation package with equity opportunities.
- Flexible remote/hybrid work culture in the heart of San Francisco.
- Access to state-of-the-art computing infrastructure and research tools.
If you are a technical leader passionate about the intersection of data science and cloud architecture, we want to hear from you.
Responsibilities
- Design and architect scalable AI/ML systems and infrastructure using modern cloud-native technologies.
- Lead the end-to-end machine learning lifecycle, from data ingestion and model training to deployment and monitoring.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to translate business requirements into technical solutions.
- Establish best practices for code quality, testing, and CI/CD pipelines within the AI domain.
- Ensure the ethical use of AI, focusing on transparency, fairness, and data privacy compliance.
- Stay abreast of the latest advancements in Deep Learning, NLP, and Generative AI to drive innovation.
Qualifications
- 7+ years of experience in software engineering, with at least 4 years specializing in Machine Learning/AI systems architecture.
- Strong proficiency in Python, PyTorch, or TensorFlow.
- Expert knowledge of cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Demonstrated experience designing high-availability, low-latency distributed systems.
- Experience with MLOps tools and deployment pipelines (MLflow, Airflow, Kubeflow).
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.
- Master’s degree in Computer Science, Artificial Intelligence, or a related field is preferred.