Job Description
We are seeking a visionary Senior Future-Ready AI Engineer to join our elite team at Chronos Systems. As we prepare for the technological paradigm shift of 2026, we are building the infrastructure for an autonomous, intelligent future. You will be at the forefront of Generative AI, Large Language Models (LLMs), and scalable neural architectures.
In this role, you will not just write code; you will architect the cognitive backbone of our next-generation applications. You will work in a high-performance environment with cutting-edge hardware, collaborating with top-tier researchers and product strategists to solve humanity's most complex problems through scalable machine learning.
Why Join Us?
- Work on projects that define the roadmap for 2026 and beyond.
- Competitive compensation package and equity options.
- Unlimited PTO and remote-first flexibility.
- Access to state-of-the-art GPU clusters and research libraries.
Responsibilities
- Architect and deploy scalable, production-ready AI models using PyTorch and TensorFlow, focusing on low-latency inference for real-time applications.
- Lead the research and implementation of cutting-edge Generative AI techniques, including fine-tuning LLMs and developing RAG (Retrieval-Augmented Generation) pipelines.
- Mentor junior engineers and data scientists, fostering a culture of innovation, code quality, and continuous learning.
- Collaborate with cross-functional teams (Product, Design, DevOps) to translate complex AI concepts into intuitive user experiences.
- Optimize model performance and reduce computational costs while maintaining high accuracy and robustness.
- Ensure ethical AI practices, including bias mitigation, data privacy, and transparency in algorithmic decision-making.
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, deep learning, or artificial intelligence.
- Strong proficiency in Python, C++, and SQL.
- Extensive experience with deep learning frameworks (PyTorch, TensorFlow, JAX) and MLOps tools (MLflow, Kubeflow, Docker, Kubernetes).
- Proven track record of deploying large-scale models to cloud environments (AWS, GCP, or Azure).
- Deep understanding of NLP, Computer Vision, or Reinforcement Learning.
- Exceptional problem-solving skills and the ability to work in a fast-paced, dynamic startup environment.