Job Description
We are at the forefront of the artificial intelligence revolution. As we look toward the technological horizon of 2026, we are seeking a Senior AI Architect to lead our next-generation model development initiatives. This is not just a job; it is a mission to engineer the intelligence of tomorrow.
In this pivotal role, you will design and implement cutting-edge Large Language Models (LLMs) and autonomous agents capable of solving complex, unstructured problems in real-time. You will bridge the gap between theoretical research and production-grade deployment, ensuring our systems are scalable, secure, and ethically aligned with the future of human-AI collaboration.
Why Join Us?
We offer a competitive benefits package, including equity packages, flexible remote work options, and access to state-of-the-art compute infrastructure. If you are passionate about pushing the boundaries of what AI can achieve, we want to hear from you.
Responsibilities
- Architect & Deploy: Design and deploy scalable generative AI models and LLMs optimized for high-performance inference.
- Model Optimization: Fine-tune pre-trained models for specific niche applications to improve accuracy and reduce latency.
- Research Implementation: Translate the latest academic research in deep learning into practical, production-ready codebases.
- MLOps Pipeline: Build and maintain robust CI/CD pipelines for model training, testing, and deployment using Kubernetes and cloud-native services.
- Ethical AI: Implement guardrails and safety protocols to ensure AI outputs adhere to ethical standards and regulatory compliance.
- Team Leadership: Mentor junior engineers and data scientists, conducting code reviews and technical architecture discussions.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related technical field.
- Technical Skills: Deep expertise in Python, PyTorch, or TensorFlow. Experience with Hugging Face Transformers and LangChain is required.
- Experience: Minimum of 5+ years of experience in machine learning engineering or applied AI research.
- Infrastructure: Proficiency in MLOps tools (Docker, Kubernetes, AWS SageMaker) and cloud platforms (AWS/GCP/Azure).
- Problem Solving: Strong ability to debug complex distributed systems and optimize neural network architectures for efficiency.
- Communication: Excellent verbal and written communication skills to articulate technical concepts to non-technical stakeholders.