Job Description
Shape the Future of Intelligence.
We are Apex Neural Systems, a pioneer in next-generation artificial intelligence. As we prepare for the paradigm shift of 2026, we are seeking a visionary Lead AI Architect to design the neural infrastructures that will define the next decade of human-computer interaction.
In this role, you will not just build models; you will architect the very fabric of our generative intelligence ecosystem. You will bridge the gap between theoretical breakthroughs and scalable, production-ready systems. If you are driven by the challenge of solving complex problems at the intersection of deep learning, ethics, and high-performance computing, we want to talk to you.
Why join us?
We offer a competitive compensation package, equity options, and the opportunity to work on cutting-edge projects that will influence the trajectory of AI globally.
Responsibilities
- Architect Scalable AI Pipelines: Design and oversee the implementation of robust, distributed systems for training and deploying large-scale Generative AI models and Large Language Models (LLMs).
- Prompt Engineering & Optimization: Lead initiatives in advanced prompt engineering, fine-tuning strategies, and retrieval-augmented generation (RAG) architectures to maximize model performance.
- Ethical AI Governance: Establish and enforce best practices for AI ethics, bias mitigation, and safety protocols to ensure responsible deployment of autonomous systems.
- Technical Strategy: Define the technical roadmap for AI infrastructure, evaluating emerging technologies and selecting the optimal tools (PyTorch, TensorFlow, JAX, etc.) for our stack.
- Team Leadership & Mentorship: Mentor a team of junior data scientists and ML engineers, fostering a culture of innovation, continuous learning, and technical excellence.
- Cross-Functional Collaboration: Partner with product managers, engineers, and stakeholders to translate complex AI capabilities into tangible business value and user-centric features.
- Performance Tuning: Continuously monitor, evaluate, and optimize model latency, throughput, and cost-efficiency in cloud environments.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Machine Learning, Mathematics, or a related field, or equivalent practical experience.
- Experience: 7+ years of experience in software engineering and machine learning, with at least 3 years in a lead or architect role.
- Technical Expertise: Deep understanding of deep learning architectures, neural network optimization, and natural language processing (NLP).
- Programming: Proficiency in Python and C++, with experience in cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Frameworks: Extensive hands-on experience with Hugging Face Transformers, LangChain, and distributed training frameworks.
- Problem Solving: Exceptional ability to troubleshoot complex system bottlenecks and architectural challenges in high-pressure environments.
- Communication: Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.