Job Description
We are seeking a visionary Senior AI Architect to spearhead the development of our proprietary 2026 Core Engine. Join a team pushing the boundaries of generative intelligence and predictive modeling to define the technological landscape of the future.
In this pivotal role, you will design scalable neural architectures and lead the research into next-generation Large Language Models (LLMs). You will collaborate with cross-functional teams to integrate AI solutions into complex systems, ensuring high performance, security, and ethical compliance.
Why join us?
- Work on cutting-edge technology that shapes the year 2026 and beyond.
- Competitive compensation package and equity opportunities.
- Flexible remote-first work culture.
Responsibilities
- Architecture Design: Design and implement scalable, distributed machine learning pipelines and neural network architectures for the 2026 Core Initiative.
- Research & Development: Lead research into emerging AI methodologies, including generative AI, reinforcement learning, and autonomous agents.
- System Optimization: Optimize model inference latency and resource utilization to ensure real-time performance at scale.
- Collaboration: Partner with software engineers, data scientists, and product managers to translate technical requirements into robust engineering solutions.
- Best Practices: Establish and enforce coding standards, documentation protocols, and deployment strategies for AI systems.
- Mentorship: Mentor junior engineers and data scientists, fostering a culture of continuous learning and innovation.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
- Experience: 7+ years of experience in software engineering with a strong focus on machine learning and AI.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and experience with distributed computing frameworks (Spark, Kubernetes).
- Domain Knowledge: Deep understanding of NLP, Computer Vision, or Deep Reinforcement Learning.
- Cloud Expertise: Proven experience deploying models on cloud platforms (AWS, GCP, or Azure).
- Problem Solving: Exceptional ability to solve complex technical problems and troubleshoot system failures.