Job Description
Are you ready to build the AI of tomorrow?
Nebula Dynamics is a cutting-edge technology firm pioneering the next generation of autonomous intelligence. We are seeking a visionary Future-Ready AI Architect (2026 Vision) to lead our research and engineering efforts in building self-improving machine learning systems.
In this pivotal role, you will bridge the gap between theoretical AI research and scalable production systems. You will define the architectural standards for our Agentic AI platforms, ensuring our solutions are not just smart today, but resilient and adaptive for the future.
Why join us?
- Work on projects that define the trajectory of AI for the next decade.
- Competitive compensation and equity package.
- Flexible remote-first culture with premium benefits.
If you have a passion for pushing the boundaries of what's possible with Large Language Models (LLMs) and Multi-Agent Systems, we want to meet you.
Responsibilities
- Design and architect scalable, high-performance AI systems capable of handling complex, multi-agent workflows.
- Lead the research and implementation of novel algorithms to enhance model reasoning, accuracy, and hallucination reduction.
- Optimize model inference latency and cost efficiency for real-time applications.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to translate research into production-grade software.
- Establish best practices for MLOps, ensuring robust monitoring, logging, and version control for all AI models.
- Predict and integrate emerging AI technologies (e.g., Quantum AI interfaces) into our existing ecosystem.
Qualifications
- Master’s or PhD in Computer Science, Artificial Intelligence, or a related quantitative field (or equivalent practical experience).
- Minimum of 5+ years of professional experience in Machine Learning Engineering or AI Architecture.
- Extensive proficiency in Python, PyTorch, or TensorFlow.
- Deep understanding of LLM architectures (Transformers, GPT, BERT), RAG pipelines, and fine-tuning methodologies.
- Experience with deployment technologies such as Docker, Kubernetes, and AWS SageMaker or Google Vertex AI.
- Strong problem-solving skills with a focus on edge cases and system scalability.