Job Description
Welcome to Nexus 2026 Solutions, where we are architecting the intelligence of the future. We are not just building software; we are defining the technological landscape for the year 2026 and beyond. As a Senior AI Engineer, you will lead the charge in developing scalable, robust, and ethical Artificial Intelligence systems that solve complex global challenges.
We are looking for a visionary engineer who is passionate about the intersection of deep learning, generative AI, and real-world applications. If you thrive in a fast-paced, high-impact environment and want to leave a legacy in the tech industry, we want to hear from you.
Why Join Us?
- Work on cutting-edge projects that shape the future.
- Competitive compensation and equity packages.
- Flexible remote-first culture with premium office amenities in the heart of San Francisco.
- Access to the latest hardware and cloud infrastructure.
Responsibilities
- Design, train, and deploy state-of-the-art machine learning models (NLP, Computer Vision, or Reinforcement Learning) optimized for production environments.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to translate business requirements into technical AI solutions.
- Optimize model inference and training pipelines to reduce latency and improve scalability across cloud environments.
- Conduct rigorous research to stay ahead of emerging trends in AI, including Large Language Models (LLMs) and autonomous agents.
- Mentor junior engineers and conduct code reviews to ensure high standards of technical excellence.
- Ensure data privacy, security, and ethical AI practices are embedded in every layer of the system.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field (PhD preferred).
- 5+ years of professional experience in Machine Learning Engineering or Data Science.
- Strong proficiency in Python, PyTorch, TensorFlow, or JAX.
- Experience with MLOps tools (Kubernetes, Docker, MLflow) and cloud platforms (AWS, GCP, or Azure).
- Deep understanding of statistical modeling and algorithm design.
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.
- Proven track record of shipping production-grade AI applications.