Job Description
Are you ready to build the future?
We are seeking a visionary Senior AI Engineer to lead our groundbreaking Project 2026. This is not just a job; it is a mission to architect the next generation of intelligent systems that will define the technological landscape for years to come. If you are passionate about pushing the boundaries of what is possible in Machine Learning and Generative AI, we want to hear from you.
As a key member of the Project 2026 team, you will work in a dynamic, high-performance environment where innovation is encouraged, and challenges are opportunities. You will be responsible for developing scalable algorithms and integrating cutting-edge AI models into production systems.
Responsibilities
- Lead Architecture: Design and implement scalable machine learning infrastructure for the 2026 roadmap, ensuring high availability and performance.
- Model Development: Develop and fine-tune proprietary Large Language Models (LLMs) and Generative AI frameworks.
- Production Deployment: Optimize models for low-latency inference and deploy them using containerization (Docker/Kubernetes) and cloud-native services.
- Research & Innovation: Stay at the forefront of AI trends, researching new techniques (e.g., Transformers, Diffusion Models) to improve system capabilities.
- Collaboration: Work closely with product managers, data scientists, and software engineers to translate business needs into technical solutions.
- Mentorship: Guide junior engineers, conduct code reviews, and foster a culture of technical excellence and continuous learning.
Qualifications
- Education: Master’s degree in Computer Science, Engineering, Mathematics, or a related technical field (PhD preferred).
- Experience: 5+ years of professional experience in AI/ML engineering, with a focus on NLP or Computer Vision.
- Programming: Expert proficiency in Python, with deep knowledge of PyTorch, TensorFlow, or JAX.
- Cloud & DevOps: Experience deploying models on AWS, GCP, or Azure using services like SageMaker or Vertex AI.
- Technical Depth: Strong understanding of deep learning principles, neural network architectures, and distributed systems.
- Soft Skills: Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.