Job Description
We are looking for a visionary Senior AI Engineer to spearhead the technological roadmap for 2026. As we prepare to launch our next generation of autonomous systems, you will be responsible for designing and implementing cutting-edge artificial intelligence solutions that push the boundaries of what is possible. Join a team of world-class engineers and researchers dedicated to shaping the future of intelligent automation.
Why Join Us?
We offer a competitive salary, comprehensive health benefits, and a flexible remote-first culture. You will have the opportunity to work on high-impact projects that define the industry standard for the coming years.
Responsibilities
- Lead AI Strategy: Define and execute the technical vision for our 2026 AI roadmap, focusing on Generative AI and predictive modeling.
- Model Development: Design, train, and deploy advanced machine learning models using Python and deep learning frameworks.
- System Architecture: Architect scalable and resilient ML pipelines that handle millions of data points with low latency.
- Research & Innovation: Stay ahead of the curve by researching emerging AI trends and integrating them into our product suite.
- Team Leadership: Mentor junior engineers and collaborate with product managers to translate complex requirements into technical solutions.
- Optimization: Continuously monitor model performance and optimize infrastructure for cost and speed.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field.
- Experience: 5+ years of professional experience in AI/ML engineering, with a focus on large language models or computer vision.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and SQL.
- Problem Solving: Strong ability to troubleshoot complex technical issues and debug deep learning models.
- Communication: Excellent written and verbal communication skills to present technical concepts to non-technical stakeholders.
- Tools: Experience with cloud platforms (AWS, GCP, or Azure) and MLOps tools (Docker, Kubernetes, MLflow).