Job Description
Welcome to 2026 Technologies, a pioneering force in the future of intelligent systems. We are not just building the tools of tomorrow; we are defining the trajectory of artificial intelligence for the next decade. As a Senior AI Architect, you will lead the charge in developing scalable, ethical, and groundbreaking neural architectures that power our next generation of products.
In this role, you will work at the intersection of deep learning research and practical engineering. You will be responsible for designing the core infrastructure that supports our generative AI models and autonomous agents. If you are passionate about pushing the boundaries of what is possible in machine learning and want to leave a lasting impact on the industry, we want to hear from you.
What You'll Do:
- Lead System Design: Architect robust, scalable, and secure AI systems capable of processing petabytes of data in real-time.
- Model Optimization: Oversee the training and fine-tuning of large language models (LLMs) to ensure peak performance and accuracy.
- R&D Leadership: Collaborate with our research team to prototype cutting-edge algorithms and evaluate emerging technologies.
- Team Mentorship: Guide junior engineers and data scientists, fostering a culture of innovation and continuous learning.
- Ethical AI Implementation: Ensure all deployed models adhere to strict ethical guidelines and fairness standards.
Responsibilities
- Design and implement scalable deep learning pipelines using Python, PyTorch, and TensorFlow.
- Conduct performance analysis and optimization of existing models to reduce latency and improve throughput.
- Define technical roadmaps and architectural standards for the AI department.
- Collaborate with product managers to translate business requirements into technical specifications.
- Stay abreast of the latest advancements in AI research and integrate relevant innovations into our stack.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field (PhD preferred).
- Minimum of 7 years of professional experience in machine learning engineering or AI architecture.
- Deep expertise in Natural Language Processing (NLP) and Large Language Models (LLMs).
- Strong proficiency in distributed computing frameworks such as Apache Spark or Kubernetes.
- Proven track record of deploying AI models into production environments at scale.
- Excellent problem-solving skills and the ability to work in a fast-paced, agile environment.