Job Description
Are you a visionary engineer ready to shape the next decade of technology? Apex Future Systems is seeking a Senior AI Engineer to lead the development of our proprietary generative AI platforms. We are building the infrastructure for 2026 and beyond, focusing on ethical AI, scalable deep learning models, and next-gen natural language processing.
In this role, you will bridge the gap between theoretical research and production-grade engineering. You will work alongside world-class data scientists and product leaders to deploy AI solutions that solve real-world problems. Join us in redefining the future of work and interaction.
Why join Apex Future Systems?
- Competitive salary and equity package.
- Unlimited PTO and flexible remote-first culture.
- Access to cutting-edge hardware and cloud resources.
- Professional development and learning stipend.
Responsibilities
- Architect and deploy scalable machine learning models for large-scale production environments.
- Lead the end-to-end lifecycle of AI projects, from data ingestion and preprocessing to model training, evaluation, and deployment.
- Collaborate with cross-functional teams to identify opportunities for AI-driven automation and efficiency.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and innovation.
- Stay abreast of the latest advancements in AI research and implement them into our product roadmap.
- Ensure model fairness, transparency, and robustness in compliance with industry regulations.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related field (or equivalent professional experience).
- Minimum of 5+ years of professional experience in machine learning and deep learning engineering.
- Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Extensive experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Proven track record of deploying NLP or Computer Vision models to production.
- Experience with MLOps tools and pipelines (MLflow, Kubeflow, etc.).
- Excellent problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.