Job Description
We are seeking a visionary Senior AI Architect to lead the strategic development of Project 2026. As a pioneer in next-generation predictive intelligence, Chronos Dynamics is building the infrastructure for the future. In this pivotal role, you will define the architectural roadmap for our proprietary neural networks and oversee the deployment of large-scale AI solutions that will define the industry standard for 2026 and beyond.
Join a elite team of researchers and engineers pushing the boundaries of what is possible in machine learning and cognitive computing.
Responsibilities
- System Architecture: Design and implement scalable, high-performance AI infrastructure and cloud-native architectures for Project 2026.
- Leadership: Mentor a team of data scientists and ML engineers, fostering a culture of innovation and technical excellence.
- Model Optimization: Oversee the training, fine-tuning, and deployment of state-of-the-art generative models and NLP systems.
- Strategic Planning: Collaborate with executive stakeholders to translate business requirements into technical roadmaps.
- R&D: Stay ahead of emerging trends in AI, including quantum computing integration and edge AI, to ensure our solutions remain cutting-edge.
- Performance Tuning: Monitor model performance in production environments, ensuring high availability and minimal latency.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Artificial Intelligence, Mathematics, or a related field.
- Experience: Minimum of 8 years of professional experience in software engineering, specifically 5+ years in AI/ML architecture.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and experience with distributed computing frameworks (e.g., Kubernetes, Apache Spark).
- Communication: Exceptional ability to communicate complex technical concepts to non-technical stakeholders and cross-functional teams.
- Problem Solving: Proven track record of solving complex, ambiguous problems in high-pressure environments.
- Tools: Familiarity with MLOps tools (e.g., MLflow, Airflow) and cloud platforms (AWS, GCP, or Azure).