Job Description
We are at the forefront of the Artificial Intelligence revolution, defining the landscape for 2026 and beyond. At FutureScale Systems, we are building the next generation of autonomous agents and generative intelligence systems. We are seeking a visionary Lead AI Architect to join our elite engineering team and spearhead the development of scalable, high-performance Large Language Models (LLMs) and neural architectures.
In this role, you will not just manage code; you will architect the future of human-machine interaction. You will bridge the gap between theoretical deep learning and production-grade systems, ensuring our solutions are robust, ethical, and infinitely scalable.
Responsibilities
- Architect and design end-to-end Generative AI pipelines, from model training and fine-tuning to deployment and inference optimization.
- Lead a team of world-class ML engineers and researchers in building state-of-the-art Neural Networks and NLP models.
- Implement and optimize RAG (Retrieval-Augmented Generation) architectures to enhance model accuracy and relevance.
- Establish best practices for MLOps, ensuring seamless CI/CD pipelines and model versioning.
- Collaborate with product and design teams to translate complex AI capabilities into intuitive user experiences.
- Drive technical decision-making regarding cloud infrastructure (AWS/GCP) and hardware acceleration (GPUs/TPUs).
- Stay ahead of the curve in emerging AI trends to keep FutureScale Systems at the cutting edge of technology.
Qualifications
- Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field.
- 5+ years of professional experience in Deep Learning, PyTorch, or TensorFlow with a focus on NLP.
- Extensive experience designing and deploying Large Language Models (GPT-4, Llama 3, etc.) and Embedding models.
- Strong proficiency in Python and C++ for high-performance computing.
- Proven track record of leading engineering teams and mentoring junior developers.
- Familiarity with data engineering, vector databases (Pinecone, Milvus), and cloud-based AI services.
- Demonstrated ability to think critically about AI ethics, bias mitigation, and safety in production environments.