Job Description
We are building the intelligence layer for the next decade. Nexus Future Labs is seeking a visionary Senior AI/ML Engineer to spearhead the development of our proprietary Generative AI suite, designed to scale through 2026 and beyond.
In this role, you won't just maintain models; you will architect the future of autonomous agents, multimodal learning systems, and ethical AI frameworks. We are looking for a technologist who is obsessed with optimization, scalability, and pushing the boundaries of what Large Language Models (LLMs) can achieve in real-world enterprise environments.
Join us in Austin, Texas, and help define the standard for AI engineering in the next era of the internet.
Responsibilities
- Architect Advanced AI Solutions: Design and implement scalable LLM architectures and fine-tuning pipelines using PyTorch and TensorFlow.
- Optimize Inference: Engineer high-performance, low-latency inference systems to handle millions of daily requests efficiently.
- Multimodal Development: Lead the integration of vision and language models to create robust, multimodal AI agents.
- MLOps Implementation: Establish CI/CD pipelines and robust MLOps infrastructure to automate model training, deployment, and monitoring.
- Ethical AI Compliance: Develop and enforce guardrails to ensure model outputs are safe, unbiased, and compliant with emerging regulations.
- Research & Prototyping: Stay ahead of 2026 industry trends, conducting POCs on cutting-edge research papers and integrating them into our production stack.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related technical field (or equivalent practical experience).
- Core Languages: Strong proficiency in Python, with deep experience in C++ for high-performance computing tasks.
- Model Frameworks: Extensive hands-on experience with Hugging Face Transformers, LangChain, and RAG (Retrieval-Augmented Generation) architectures.
- Infrastructure: Proven track record deploying models on cloud platforms (AWS/GCP/Azure) using Kubernetes and Docker.
- Mathematical Aptitude: Solid foundation in Linear Algebra, Calculus, and Probability Theory.
- Problem Solving: Demonstrated ability to troubleshoot complex system bottlenecks and optimize computational graphs.