Job Description
We are pioneering the next generation of intelligent automation with our proprietary 2026 Protocol. As a Lead AI Architect, you will be at the forefront of integrating large-scale Large Language Models (LLMs) into autonomous agent workflows.
In this pivotal role, you will bridge the gap between theoretical AI research and production-grade engineering, ensuring our systems are scalable, secure, and capable of solving complex global challenges.
Why Join Us?
- Shape the roadmap for the AI infrastructure of tomorrow.
- Work with state-of-the-art models and proprietary datasets.
- Competitive compensation package with equity opportunities.
Responsibilities
- Architect and deploy scalable Agentic AI systems designed for the 2026 ecosystem, focusing on autonomy and self-correction.
- Optimize inference latency and cost-efficiency for LLM workloads using techniques such as quantization and model distillation.
- Design robust RAG (Retrieval-Augmented Generation) pipelines to ensure data accuracy and relevance.
- Collaborate with cross-functional teams to define technical specifications and API standards.
- Implement rigorous testing frameworks for AI reliability, including hallucination detection and bias mitigation.
- Mentor junior engineers and data scientists, fostering a culture of continuous innovation.
- Ensure compliance with data privacy regulations and ethical AI guidelines.
Qualifications
- Master’s or PhD in Computer Science, Artificial Intelligence, or a related quantitative field.
- 7+ years of professional experience in Machine Learning Engineering, with at least 3 years in a lead or architect role.
- Deep expertise in Python, PyTorch, or TensorFlow, with hands-on experience deploying models in production environments.
- Proven track record of working with Vector Databases (e.g., Pinecone, Milvus) and LLM orchestration frameworks (e.g., LangChain, LlamaIndex).
- Strong understanding of cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Experience with MLOps tools (MLflow, Airflow) and CI/CD pipelines.
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.