Job Description
We are building the infrastructure for 2026 and beyond. As a Senior AI Architect at Apex Horizon Systems, you won't just manage algorithms; you will architect the neural networks that define the next era of intelligent automation. We are seeking a visionary engineer to lead our core R&D team in developing scalable, ethical, and transformative AI solutions.
In this role, you will bridge the gap between theoretical machine learning breakthroughs and production-grade software engineering. You will oversee the deployment of Large Language Models (LLMs), vector databases, and autonomous agent systems designed to handle the complex demands of the future web.
Why Join Us?
- Future-First Technology: Work with cutting-edge tech stacks before they hit the mainstream.
- Impactful Work: Your code will directly influence how millions of users interact with AI in the coming years.
- Competitive Compensation: Top-tier salary plus performance bonuses.
Responsibilities
- Architect and implement end-to-end machine learning pipelines designed to scale for 2026 workloads.
- Lead the integration of Generative AI models into enterprise-level products, ensuring high performance and low latency.
- Design robust data pipelines for real-time training and inference, utilizing distributed computing frameworks.
- Define best practices for model governance, bias mitigation, and AI safety.
- Collaborate with cross-functional teams (Product, Engineering, Design) to translate business requirements into technical AI solutions.
- Mentor junior engineers and data scientists, fostering a culture of innovation and technical excellence.
Qualifications
- PhD or Master’s degree in Computer Science, Artificial Intelligence, or a related quantitative field.
- 7+ years of experience in software engineering, with at least 3 years focused on AI/ML architecture.
- Deep expertise in Python, TensorFlow, PyTorch, and modern MLOps tools (Kubeflow, MLflow).
- Strong understanding of distributed systems, cloud architecture (AWS/GCP), and containerization (Docker/Kubernetes).
- Experience with LLMs, RAG (Retrieval-Augmented Generation), and vector databases (Pinecone, Milvus).
- Proven track record of shipping production-ready AI products to market.