Job Description
Join Apex Logic Systems as we redefine the boundaries of artificial intelligence. We are currently seeking a Senior AI Architect to spearhead the development of our flagship generative models, specifically targeting the aggressive 2026 roadmap milestones. This is not just a job; it is a mission to engineer the intelligence that will power the next decade of enterprise solutions.
In this pivotal role, you will bridge the gap between cutting-edge research and scalable production engineering. You will lead a team of brilliant minds in building, training, and deploying state-of-the-art Large Language Models (LLMs) and multimodal systems.
Key Highlights:
- Architect the next generation of AI infrastructure for the 2026 release cycle.
- Drive innovation in model efficiency, reducing inference costs while maximizing accuracy.
- Collaborate cross-functionally with product, security, and engineering teams.
Responsibilities
- Lead the architectural design and implementation of proprietary Large Language Models (LLMs) and diffusion models.
- Optimize model training pipelines using distributed computing frameworks (e.g., Ray, PyTorch Distributed) to handle petabytes of training data.
- Develop and implement advanced Reinforcement Learning from Human Feedback (RLHF) loops to enhance model alignment and safety.
- Establish and enforce best practices for MLOps, ensuring models are reproducible, version-controlled, and deployed seamlessly.
- Conduct rigorous code reviews and technical mentoring for a team of AI engineers and researchers.
- Identify and mitigate potential biases and ethical risks in AI outputs.
Qualifications
- Master’s or Ph.D. in Computer Science, Artificial Intelligence, or a related quantitative field.
- 7+ years of experience in Machine Learning, Deep Learning, or NLP, with at least 3 years in a leadership or architectural capacity.
- Expert proficiency in Python, PyTorch, and TensorFlow.
- Deep understanding of Transformer architectures, attention mechanisms, and generative AI principles.
- Strong experience with cloud infrastructure (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Proven ability to translate complex research papers into production-ready code.