Job Description
Are you ready to define the intelligence of the future? Neural Nexus Technologies is seeking a visionary Senior AI Engineer to architect the machine learning systems that will power our platform through 2026 and beyond. We are building the next generation of generative AI tools, and we need a leader who understands not just the code, but the trajectory of the industry.
In this role, you will lead the design and deployment of cutting-edge Large Language Models (LLMs) and autonomous agents. You will work in a high-performance environment that values innovation, speed, and ethical AI development. If you are passionate about pushing the boundaries of what is possible in artificial intelligence, this is your opportunity to shape the decade ahead.
Why join us?
- Work on scalable infrastructure that processes petabytes of data.
- Competitive compensation package including equity.
- Flexible work arrangements and top-tier benefits.
- Access to the latest hardware for AI research (H100 clusters, GPUs).
Responsibilities
- Model Development: Design, train, and fine-tune state-of-the-art foundation models for specific enterprise applications.
- System Architecture: Build robust, scalable pipelines for data ingestion, processing, and model serving.
- Optimization: Reduce inference costs and latency while maximizing model accuracy.
- R&D Leadership: Conduct research on novel architectures, including multimodal models and reinforcement learning.
- Collaboration: Partner with product and engineering teams to integrate AI capabilities into user-facing products.
- Ethics & Compliance: Ensure AI systems adhere to safety guidelines and ethical standards.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related field.
- Experience: 5+ years of professional experience in AI/ML engineering.
- Languages: Proficiency in Python, C++, and SQL.
- Frameworks: Deep expertise in PyTorch, TensorFlow, or JAX.
- Knowledge: Strong understanding of NLP, Transformers, and vector databases.
- Tools: Experience with MLOps tools (Kubeflow, MLflow, Docker, Kubernetes).