Job Description
Welcome to the future of intelligence. Apex Neural Systems is at the forefront of defining the Artificial Intelligence landscape for 2026. We are seeking a visionary Senior AI Architect to spearhead the development of our next-generation Large Language Model (LLM) infrastructure. In this pivotal role, you will bridge the gap between theoretical research and production-scale deployment, building systems capable of agentic workflows, continuous learning, and multi-modal reasoning.
Why Join Us?
- Industry Leadership: Work on the core technology stack expected to define the industry standard by 2026.
- Impact: Your work will directly power enterprise-grade solutions for Fortune 500 clients.
- Equity Package: Competitive RSU grants and performance bonuses.
We are looking for a builder who is obsessed with optimization, latency reduction, and model safety. If you want to shape the next era of AI, this is your stage.
Responsibilities
- Lead Model Development: Architect and fine-tune state-of-the-art transformer models (e.g., Llama 3, GPT-4 equivalents) for specific vertical use cases.
- RAG & Vector Databases: Design robust Retrieval-Augmented Generation (RAG) pipelines to ensure accuracy and reduce hallucinations.
- Infrastructure Optimization: Implement quantization techniques (INT8/INT4) and model distillation strategies to deploy high-performance models on edge devices.
- System Scalability: Build and maintain scalable inference servers using Kubernetes and cloud-native architectures (AWS/GCP).
- AI Safety & Ethics: Implement guardrails and alignment techniques to ensure safe and responsible AI deployment.
- Research Integration: Translate cutting-edge academic research papers into production-ready code within 3-6 months of release.
Qualifications
- Experience: 5+ years of professional experience in Machine Learning, NLP, or Deep Learning.
- Technical Stack: Proficiency in Python, PyTorch, or TensorFlow. Experience with LangChain or LlamaIndex is highly preferred.
- Education: M.S. or Ph.D. in Computer Science, Mathematics, or a related field (or equivalent industry experience).
- Model Engineering: Deep understanding of Transformer architectures, attention mechanisms, and tokenization strategies.
- Tools: Hands-on experience with MLOps tools (MLflow, Kubeflow), version control (Git), and CI/CD pipelines.
- Communication: Excellent ability to communicate complex technical concepts to non-technical stakeholders.