Job Description
We are not just building software for today; we are architecting the technological infrastructure for the year 2026. QuantumLeap Innovations is seeking a visionary Lead AI Architect to define the roadmap for our next-generation autonomous systems. If you thrive on solving complex problems and want to shape the future of AI agents, this is your opportunity to lead the charge.
In this role, you will bridge the gap between theoretical AI research and production-grade engineering, ensuring our products are at the forefront of the 2026 tech landscape.
Why Join Us?
- Work with state-of-the-art Large Language Models (LLMs) and multimodal agents.
- Competitive equity package and performance bonuses.
- Flexible remote and on-site hybrid options in the heart of San Francisco.
- Direct access to executive leadership to influence product direction.
Responsibilities
- Define the 2026 Tech Stack: Lead the architectural vision for our AI ecosystem, selecting and integrating cutting-edge frameworks to support future scalability.
- Build Autonomous Agents: Design and deploy sophisticated AI agents capable of complex reasoning, tool use, and continuous learning without human intervention.
- Roadmap Strategy: Collaborate with the CTO and product team to define the strategic roadmap, prioritizing features that deliver maximum impact for the 2026 release cycle.
- System Optimization: Oversee the optimization of model inference pipelines, ensuring low-latency performance and cost efficiency at scale.
- Team Leadership: Mentor a high-performing team of ML engineers and data scientists, fostering a culture of innovation and technical excellence.
- Research & Development: Stay ahead of industry trends, conducting POCs (Proof of Concepts) for emerging AI paradigms.
Qualifications
- Experience: 8+ years of experience in software engineering, with at least 5 years specifically in AI, Machine Learning, or Deep Learning architecture.
- Technical Mastery: Expert proficiency in Python, PyTorch, or TensorFlow. Strong understanding of distributed systems and cloud infrastructure (AWS, GCP, or Azure).
- AI Expertise: Deep knowledge of NLP, LLMs, RAG (Retrieval-Augmented Generation), and prompt engineering best practices.
- Leadership: Proven track record of leading engineering teams through complex technical challenges and product launches.
- Communication: Exceptional ability to translate complex technical concepts into clear strategies for non-technical stakeholders.
- Education: Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field.