Job Description
Join the 2026 Revolution.
Apex Future Systems is pioneering the next generation of autonomous intelligence. We are seeking a visionary Senior AI Architect to lead the development of scalable Generative AI and Large Language Model (LLM) infrastructures. This role is critical to our roadmap, focusing on building systems that are not only functional today but resilient and advanced for the 2026 era.
In this position, you will bridge the gap between cutting-edge research and production engineering, deploying AI agents that learn, adapt, and scale. If you are passionate about the future of technology and want to define the standards for AI in the coming years, we want to hear from you.
Why Join Apex Future Systems?
- Work on high-impact projects that shape the future of industry.
- Competitive compensation package with equity opportunities.
- Flexible remote-first culture with a hub in the heart of San Francisco.
Responsibilities
- Architect LLM Solutions: Design and deploy production-grade Generative AI applications, including Retrieval-Augmented Generation (RAG) pipelines and autonomous AI agents.
- Model Optimization: Fine-tune and optimize large language models for specific business domains, focusing on inference latency, token cost reduction, and response accuracy.
- System Scalability: Build robust, cloud-native infrastructure to support high-volume AI workloads, ensuring 99.9% uptime and seamless scalability.
- Ethical AI: Implement rigorous guardrails, bias mitigation strategies, and safety protocols to ensure responsible AI deployment.
- Research Integration: Stay ahead of the curve by integrating the latest advancements in AI research (e.g., Transformer architectures, diffusion models) into our core technology stack.
- Collaboration: Partner with product managers and engineers to translate complex AI capabilities into user-friendly features.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field, or equivalent practical experience.
- Experience: 5+ years of professional experience in Machine Learning, Artificial Intelligence, or Deep Learning engineering.
- Technical Skills: Strong proficiency in Python, PyTorch, TensorFlow, or JAX. Deep understanding of Natural Language Processing (NLP) and Transformer architectures.
- Tools: Experience with cloud platforms (AWS/GCP/Azure), containerization (Docker/Kubernetes), and MLOps frameworks (MLflow, Kubeflow).
- Problem Solving: Demonstrated ability to solve complex, ambiguous problems and translate them into scalable technical solutions.
- Communication: Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.