Job Description
We are seeking a visionary Senior AI & Machine Learning Architect to lead our initiatives for the 2026 roadmap. At Apex Future Systems, we are building the infrastructure that will define the next generation of intelligent applications. You will not just write code; you will architect the neural pathways of tomorrow. If you are passionate about pushing the boundaries of Deep Learning and Generative AI, we want to hear from you.
Why Join Us?
- Work on cutting-edge technology with a world-class team.
- Competitive compensation package and equity options.
- Flexible remote-first policy with quarterly in-person meetups.
Responsibilities
- Strategic Architecture: Design and implement scalable AI/ML infrastructure tailored for high-performance computing needs in the 2026 era.
- Model Development: Lead the end-to-end development of complex Deep Learning models, including NLP, Computer Vision, and Reinforcement Learning.
- Team Leadership: Mentor a team of junior data scientists and engineers, fostering a culture of innovation and technical excellence.
- Research Integration: Stay abreast of the latest academic research and industry trends to integrate novel techniques into production systems.
- Optimization: Drive performance optimization and reduce inference latency for real-time applications.
- Collaboration: Partner with product managers and stakeholders to translate business requirements into technical AI solutions.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, or a related quantitative field.
- Experience: Minimum of 5-7 years of experience in AI/ML engineering, with at least 2 years in a leadership or architectural role.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or JAX. Strong understanding of MLOps and cloud platforms (AWS/GCP/Azure).
- Problem Solving: Proven track record of solving complex, unstructured problems using data-driven methodologies.
- Communication: Excellent verbal and written communication skills, capable of explaining complex technical concepts to non-technical stakeholders.
- Tools: Experience with distributed computing systems (Kubernetes, Spark) and version control (Git).