Job Description
We are looking for a visionary Strategic AI & Machine Learning Architect to lead our roadmap for the 2026 Horizon. In this pivotal role, you will define the next generation of artificial intelligence infrastructure, ensuring our platforms are ready for the future of autonomous systems and generative evolution. If you are a thought leader with a passion for pushing the boundaries of what's possible in AI, Zenith Systems is the place for you.
Why Join Us?
- Future-Forward Vision: Be at the forefront of AI innovation, shaping the technology landscape for 2026 and beyond.
- Competitive Compensation: Earn a top-tier salary plus performance bonuses.
- Global Impact: Work on projects that will redefine industry standards.
We value deep expertise, creative problem-solving, and a relentless drive for excellence.
Responsibilities
- Define and execute the comprehensive AI roadmap for the 2026 Horizon, aligning technical strategy with business goals.
- Architect scalable, high-performance machine learning models and deep learning systems capable of handling enterprise-scale data.
- Lead the research and integration of emerging AI technologies, including Large Language Models (LLMs) and predictive analytics.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to drive AI adoption.
- Mentor junior architects and data scientists, fostering a culture of continuous learning and technical excellence.
- Ensure data privacy, security, and ethical AI practices are embedded in all architectural decisions.
Qualifications
- Master’s or PhD in Computer Science, Artificial Intelligence, or a related technical field.
- 10+ years of experience in software engineering, machine learning, and AI architecture.
- Deep proficiency in Python, TensorFlow, PyTorch, and SQL.
- Proven track record of leading complex AI projects from conception to production deployment.
- Strong leadership experience with the ability to influence stakeholders and manage high-performance teams.
- Experience with cloud platforms (AWS, GCP, or Azure) and MLOps pipelines.