Job Description
We are seeking a visionary Senior AI Architect to define the technological landscape of 2026. As the industry accelerates towards autonomous agents and multimodal intelligence, your role will be pivotal in architecting scalable, secure, and transformative solutions.
Why this role?
- Work on the forefront of Agentic AI and next-gen LLM orchestration.
- Shape the future of enterprise technology for the 2026 era.
- Competitive equity package and remote-first culture.
- Opportunity to lead a team of top-tier engineers.
Key Responsibilities:
- Architect and deploy large-scale generative models tailored for 2026 business needs.
- Lead the research and integration of emerging AI paradigms such as Reinforcement Learning from Human Feedback (RLHF) and Chain-of-Thought reasoning.
- Collaborate with cross-functional teams to translate complex AI concepts into robust engineering products.
- Mentor junior engineers and establish best practices for AI safety and ethics.
- Optimize model inference latency and reduce operational costs using advanced cloud infrastructure.
Qualifications:
- Master’s degree or PhD in Computer Science, Machine Learning, or a related quantitative field.
- 5+ years of professional experience building production-grade machine learning systems.
- Deep proficiency in Python, PyTorch, or TensorFlow.
- Experience with vector databases (Pinecone, Milvus) and LLM orchestration tools (LangChain, LlamaIndex).
- Strong understanding of distributed systems and cloud infrastructure (AWS/GCP/Azure).
- Proven track record of leading technical projects from conception to deployment.
Responsibilities
- Architect and deploy large-scale generative models tailored for 2026 business needs.
- Lead the research and integration of emerging AI paradigms such as Reinforcement Learning from Human Feedback (RLHF) and Chain-of-Thought reasoning.
- Collaborate with cross-functional teams to translate complex AI concepts into robust engineering products.
- Mentor junior engineers and establish best practices for AI safety and ethics.
- Optimize model inference latency and reduce operational costs using advanced cloud infrastructure.
Qualifications
- Master’s degree or PhD in Computer Science, Machine Learning, or a related quantitative field.
- 5+ years of professional experience building production-grade machine learning systems.
- Deep proficiency in Python, PyTorch, or TensorFlow.
- Experience with vector databases (Pinecone, Milvus) and LLM orchestration tools (LangChain, LlamaIndex).
- Strong understanding of distributed systems and cloud infrastructure (AWS/GCP/Azure).
- Proven track record of leading technical projects from conception to deployment.