Job Description
We are seeking a visionary Senior AI Architect (2026 Vision) to lead the next generation of intelligent systems at Apex Digital Labs. As we look toward the future of technology, we need a pioneer who can bridge the gap between theoretical AI breakthroughs and scalable, real-world applications. If you are passionate about shaping the AI landscape of 2026 and beyond, this is your opportunity to make an impact.
In this role, you will design and deploy cutting-edge machine learning architectures, focusing on Generative AI, LLM orchestration, and autonomous agent workflows. You will work closely with a team of world-class engineers and researchers to build systems that are not only powerful but also ethical and efficient.
Why Join Us?
• Competitive compensation package with equity.
• Work on high-impact projects that define the future of tech.
• Flexible remote-first culture with a premium office in San Francisco.
Responsibilities
- Architect and implement scalable AI/ML infrastructure capable of handling petabyte-scale data.
- Lead the research and development of novel Generative AI models, including fine-tuning and RAG (Retrieval-Augmented Generation) strategies.
- Optimize model inference pipelines for speed and cost-efficiency using edge computing and distributed systems.
- Establish best practices for AI ethics, safety, and bias mitigation in all deployed models.
- Mentor junior engineers and data scientists, fostering a culture of innovation and continuous learning.
- Collaborate with cross-functional teams to translate complex technical requirements into robust software solutions.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Mathematics, or a related field, or equivalent extensive industry experience.
- 7+ years of professional experience in software engineering, with a focus on Machine Learning or Artificial Intelligence.
- Deep expertise in Python, PyTorch, or TensorFlow, with a proven track record of deploying models to production.
- Extensive knowledge of Large Language Models (LLMs), Transformer architectures, and prompt engineering.
- Experience with vector databases (e.g., Pinecone, Milvus) and MLOps tools (e.g., MLflow, Kubeflow).
- Strong understanding of distributed systems, cloud computing (AWS/GCP/Azure), and containerization (Docker/Kubernetes).