Job Description
We are looking for a visionary Senior AI Engineer to lead our research division and architect the technological infrastructure for the year 2026. At Apex Horizon Systems, we don't just predict trends; we build them. You will be at the forefront of Generative AI, Agentic Workflows, and next-gen Machine Learning models.
In this role, you will bridge the gap between theoretical AI research and scalable production deployment. You will mentor a team of high-performing engineers and define the roadmap for our proprietary AI ecosystem. If you are passionate about the future of technology and want to leave a legacy in the 2026 landscape, we want to hear from you.
Why Join Us?
- Impact: Directly influence the core algorithms that will power the next decade of digital interaction.
- Equity: Competitive stock options in a high-growth startup.
- Environment: Collaborative, diverse, and focused on technical excellence.
Apply today to shape the future of AI.
Responsibilities
- Architect & Scale: Design and implement scalable machine learning pipelines and large language model (LLM) architectures optimized for the 2026 computational landscape.
- Research Leadership: Lead the R&D team in exploring cutting-edge topics such as autonomous agents, reinforcement learning, and ethical AI governance.
- Model Optimization: Improve model inference speeds, reduce latency, and enhance accuracy through fine-tuning and distillation techniques.
- Team Mentorship: Mentor junior engineers and data scientists, conducting code reviews and technical workshops.
- Strategic Roadmap: Define the technical vision for AI adoption across the company, ensuring alignment with business goals.
- Deployment: Oversee the CI/CD pipeline for MLOps, ensuring seamless deployment to cloud environments.
Qualifications
- Education: MS or PhD in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in software engineering and machine learning, with a strong portfolio of published research or deployed models.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, and experience with Hugging Face or LangChain.
- MLOps: Deep understanding of containerization (Docker), orchestration (Kubernetes), and cloud platforms (AWS/GCP/Azure).
- Problem Solving: Exceptional ability to solve complex, unstructured problems in dynamic environments.
- Communication: Ability to translate complex technical concepts into actionable business strategies.