Job Description
Are you ready to architect the future? Join 2026, a trailblazing technology firm redefining the landscape of artificial intelligence and machine learning. We are looking for a visionary Lead AI Engineer to spearhead our next generation of intelligent systems and drive innovation across our global platforms.
Why join us?
- Work with cutting-edge AI/ML frameworks and hardware.
- Competitive equity package and remote-first flexibility.
- Shape the roadmap for technologies that will define the next decade.
We are seeking a highly skilled and passionate professional to lead our AI initiatives. If you thrive in a fast-paced, high-impact environment and want to build the tools of tomorrow, apply today.
Responsibilities
- Model Development: Design, implement, and optimize state-of-the-art machine learning and deep learning models for production environments.
- Technical Leadership: Lead a team of data scientists and engineers, providing mentorship and technical guidance on best practices in AI architecture.
- System Design: Collaborate with backend and frontend teams to integrate AI models into scalable software solutions.
- Data Strategy: Oversee the data pipeline, ensuring data quality, integrity, and availability for training and inference.
- R&D: Stay ahead of industry trends, research new algorithms, and prototype innovative solutions to complex problems.
- Deployment: Manage the CI/CD pipeline for machine learning models, ensuring rapid and reliable deployment.
Qualifications
- Experience: 5+ years of experience in software engineering or data science, with at least 2 years in a lead or senior technical role.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or Scikit-learn. Strong understanding of distributed systems and cloud platforms (AWS, GCP, or Azure).
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field is highly preferred.
- Communication: Excellent verbal and written communication skills, with the ability to translate complex technical concepts to non-technical stakeholders.
- Problem Solving: Demonstrated ability to troubleshoot complex issues and deliver robust, scalable solutions under pressure.