Job Description
Welcome to the future of technology. Nexus Horizon Solutions is pioneering the next generation of artificial intelligence, and we are looking for a Future-Proof AI Lead to help us shape the landscape of 2026 and beyond. In this role, you will not just adapt to change; you will architect it.
We are seeking a visionary engineer with a deep understanding of scalable machine learning systems, AI ethics, and the technological trends defining the decade ahead. If you are ready to lead a high-impact team in a dynamic environment, we want to hear from you.
Why Join Us?
- Future-Ready Environment: Work on cutting-edge projects designed to scale through 2026 and beyond.
- Competitive Compensation: Base salary ranging from $180,000 to $260,000 USD.
- Equity Package: Significant stock options in a high-growth unicorn.
Responsibilities
- Architect and deploy scalable AI models (LLMs, Computer Vision) tailored for high-traffic enterprise environments.
- Define the technical roadmap for AI integration, ensuring alignment with 2026 industry standards and trends.
- Lead a team of data scientists and engineers, fostering a culture of innovation and continuous learning.
- Implement rigorous testing and validation frameworks to ensure model reliability and fairness.
- Collaborate with cross-functional stakeholders to translate complex business requirements into technical AI solutions.
- Mentor junior developers and conduct code reviews to maintain high engineering standards.
Qualifications
- 5+ years of experience in software engineering, with at least 3 years specifically in Machine Learning or AI architecture.
- Strong proficiency in Python, TensorFlow, PyTorch, or similar deep learning frameworks.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker/Kubernetes).
- Deep understanding of NLP, Generative AI, or Reinforcement Learning.
- Excellent problem-solving skills and the ability to thrive in fast-paced, agile environments.
- BS, MS, or PhD in Computer Science, Engineering, or a related quantitative field.