Job Description
We are seeking a visionary Senior AI Architect to spearhead our roadmap for 2026 and beyond. In this pivotal role, you will define the technical strategy for our next-generation AI systems, ensuring we remain at the forefront of innovation in a rapidly evolving landscape.
As a key leader in our engineering team, you will bridge the gap between cutting-edge theoretical AI research and production-grade systems, driving innovation that will define our product ecosystem for the coming years. If you are passionate about building scalable, intelligent solutions and want to shape the future of technology, we want to hear from you.
Responsibilities
- Architect & Strategize: Design and implement scalable AI/ML infrastructure aligned with our 2026 strategic roadmap and long-term vision.
- Model Development: Lead the design and training of Large Language Models (LLMs) and generative AI features to enhance user experiences.
- Production Optimization: Optimize model performance, reduce latency, and significantly lower inference costs for enterprise-grade deployment.
- Cross-Functional Leadership: Collaborate closely with product managers, data scientists, and full-stack engineers to translate business requirements into robust technical solutions.
- MLOps & Infrastructure: Oversee the deployment, monitoring, and maintenance of AI models using modern MLOps practices and cloud platforms.
- Talent Mentorship: Mentor junior engineers and data scientists, fostering a culture of technical excellence, continuous learning, and innovation.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience building and deploying production-level AI/ML systems.
- Programming: Expert proficiency in Python and deep experience with frameworks such as PyTorch, TensorFlow, or JAX.
- Specialization: Strong background in Natural Language Processing (NLP), Computer Vision, or Reinforcement Learning.
- Infrastructure: Proven experience with MLOps tools (Kubernetes, Docker, MLflow) and major cloud providers (AWS, GCP, or Azure).
- Soft Skills: Exceptional problem-solving skills, clear communication abilities, and a track record of leading technical initiatives.