Job Description
We are at the forefront of the AI revolution, building the foundational models that will define the next decade of technology. Apex Neural Systems is seeking a visionary Senior AI Engineer to spearhead the development of our proprietary Large Language Models and generative AI systems. You will work in a high-impact environment where your code directly shapes the future of human-machine interaction. If you are passionate about solving complex problems in deep learning, NLP, and scalable architecture, we want to hear from you.
Why Join Us?
• Work with cutting-edge technology in a fast-paced, collaborative environment.
• Competitive compensation package and equity options.
• Opportunities for rapid career growth and leadership.
Responsibilities
- Model Development: Design, train, and fine-tune large-scale generative AI models (e.g., LLMs) to achieve state-of-the-art performance in NLP tasks.
- Research & Innovation: Stay abreast of the latest academic research in AI/ML and implement novel techniques into production systems.
- System Architecture: Build scalable, efficient, and fault-tolerant pipelines for training, evaluation, and inference of AI models.
- MLOps: Implement and maintain CI/CD pipelines for machine learning, ensuring reproducibility and automated deployment.
- Cross-Functional Collaboration: Partner with product managers, data scientists, and engineers to translate business requirements into technical AI solutions.
- Mentorship: Guide and mentor junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field, with a focus on Artificial Intelligence or Machine Learning.
- Experience: 5+ years of professional experience in software engineering or data science, with at least 3 years focused on AI/ML model development.
- Technical Skills: Strong proficiency in Python, PyTorch, or TensorFlow. Deep understanding of Deep Learning architectures (Transformers, RNNs, CNNs).
- NLP Expertise: Extensive experience with Natural Language Processing techniques, tokenization, embedding methods, and LLM fine-tuning (PEFT, LoRA).
- Infrastructure: Experience with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes).
- Problem Solving: Demonstrated ability to tackle complex technical challenges and optimize model performance for speed and accuracy.