Job Description
Are you ready to architect the future of intelligence?
Apex Horizon Systems is on the hunt for a visionary Senior AI/ML Architect to spearhead our roadmap for 2026. We are building the infrastructure for the next generation of Artificial General Intelligence (AGI) and autonomous agents.
In this pivotal role, you won't just be writing code; you will define the architectural standards for scalable, ethical, and high-performance AI systems that will redefine human-machine interaction. Join a team of elite engineers and researchers dedicated to solving humanity's most complex challenges through technology.
Why join us?
- Next-Gen Technology: Work on bleeding-edge projects focused on neuromorphic computing and predictive AI.
- Impactful Work: Your solutions will directly influence the trajectory of AI adoption in enterprise sectors globally.
- Top-Tier Compensation: Competitive base salary, equity options, and comprehensive benefits package.
Responsibilities
- Lead the architectural design and implementation of large-scale Machine Learning pipelines focused on 2026-era scalability requirements.
- Define the technical vision for integrating Generative AI models with real-time data streams.
- Mentor junior engineers and data scientists, fostering a culture of innovation and technical excellence.
- Collaborate with cross-functional teams (Product, Security, Research) to mitigate AI bias and ensure ethical compliance.
- Optimize model inference latency and resource utilization to support billions of daily requests.
- Conduct research and POCs on emerging paradigms such as Federated Learning and Edge AI deployment.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related technical field.
- 10+ years of experience in Machine Learning, Deep Learning, or AI Architecture.
- Expert proficiency in Python, PyTorch, or TensorFlow with a deep understanding of distributed computing frameworks.
- Proven track record of deploying production-grade AI models that handle high concurrency and low latency.
- Strong understanding of cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker/Kubernetes).
- Experience with MLOps tools and methodologies (MLflow, Kubeflow, SageMaker).
- Excellent communication skills with the ability to translate complex technical concepts for non-technical stakeholders.