Job Description
Shape the Future of Intelligence with Nexus Core Systems
Are you ready to architect the technological landscape of 2026? Nexus Core Systems is seeking a visionary Senior AI/ML Engineer to lead our cutting-edge research and development division. We are building the foundational models for the next generation of autonomous agents and cognitive computing systems. If you are passionate about pushing the boundaries of what is possible with Generative AI and Deep Learning, we want to meet you.
Why Join Us?
• Be at the forefront of the AI revolution, defining the roadmap for 2026 and beyond.
• Work with state-of-the-art infrastructure and proprietary datasets.
• Competitive compensation package including equity and health benefits.
What You Will Do
Nexus Core Systems is looking for a leader who can translate theoretical breakthroughs into scalable, production-ready solutions.
Responsibilities
- Design, develop, and deploy advanced Machine Learning and Deep Learning models to solve complex, real-world problems.
- Lead the research and implementation of Generative AI architectures, including Large Language Models (LLMs) and diffusion models.
- Optimize model inference pipelines for speed and efficiency using MLOps best practices.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to define technical requirements.
- Stay ahead of industry trends in AI, identifying opportunities for innovation and improvement.
- Mentor junior engineers and conduct code reviews to maintain high technical standards.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Mathematics, or a related quantitative field.
- 7+ years of professional experience in software engineering and machine learning.
- Expert proficiency in Python and deep frameworks such as PyTorch or TensorFlow.
- Strong experience with MLOps tools (Docker, Kubernetes, MLflow, Airflow).
- Proven track record of deploying models that handle high-scale, high-traffic environments.
- Familiarity with cloud platforms (AWS, GCP, or Azure) and distributed systems.