Job Description
We are Veridion Tech, a pioneer in next-generation artificial intelligence systems. As we prepare to redefine the technological landscape for 2026 and beyond, we are seeking a visionary Senior AI Architect to lead our core infrastructure and model deployment strategies.
In this role, you won't just write code; you will architect the cognitive frameworks that power our enterprise solutions. We offer a competitive compensation package, equity opportunities, and the chance to work with cutting-edge hardware and software stacks in a remote-first, high-performance environment.
Why Join Us?
⢠Be at the forefront of the AI revolution.
⢠Work with a team of world-class engineers and researchers.
⢠Competitive salary and equity package.
Responsibilities
- Architect and design scalable, high-performance AI and machine learning infrastructure capable of handling petabyte-scale data.
- Lead the end-to-end development lifecycle of proprietary machine learning models, from research to production deployment.
- Define technical roadmaps for 2026, ensuring our systems remain resilient and future-proof against emerging industry standards.
- Collaborate with cross-functional teamsâincluding product managers, data scientists, and security expertsâto integrate AI capabilities seamlessly.
- Mentor junior engineers and architects, fostering a culture of technical excellence and continuous learning.
- Optimize existing models for latency, throughput, and cost-efficiency using advanced distributed computing techniques.
- Ensure strict adherence to data privacy regulations and ethical AI guidelines in all system implementations.
Qualifications
- Bachelorâs or Masterâs degree in Computer Science, Mathematics, or a related field; PhD preferred.
- 7+ years of experience in software engineering, with at least 4 years specifically focused on Machine Learning or Artificial Intelligence.
- Deep expertise in Python, PyTorch, TensorFlow, or JAX.
- Strong proficiency in designing microservices and distributed systems (e.g., Kubernetes, Docker, AWS, GCP).
- Proven track record of deploying large-scale ML models into production environments.
- Experience with MLOps tools and CI/CD pipelines.
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.