Job Description
We are building the infrastructure for the next era of technology. As we approach 2026, our mission is to integrate autonomous systems into enterprise workflows at scale. We are seeking a visionary Future-Ready AI Solutions Architect to lead our R&D initiatives and design resilient, scalable AI ecosystems.
In this role, you won't just implement existing models; you will architect the systems that define our competitive advantage in the coming years. You will bridge the gap between theoretical AI research and production-grade deployment, ensuring our solutions are robust, ethical, and future-proof.
Responsibilities
- Define and execute the technical roadmap for AI integration, specifically targeting 2026 scalability goals.
- Design and deploy distributed machine learning systems using cloud-native technologies.
- Lead a team of data scientists and engineers to translate business requirements into technical solutions.
- Oversee the end-to-end data pipeline, ensuring data integrity and real-time processing capabilities.
- Conduct rigorous testing and validation of AI models to ensure accuracy and compliance with industry standards.
- Collaborate with product managers to prioritize features that drive user engagement and retention.
- Stay ahead of emerging AI trends and technologies to continuously innovate our product suite.
Qualifications
- 10+ years of experience in software engineering, with at least 5 years specifically in AI/ML architecture.
- Deep expertise in Python, TensorFlow, PyTorch, and modern cloud platforms (AWS, GCP, or Azure).
- Strong understanding of distributed systems, microservices, and containerization (Docker, Kubernetes).
- Proven track record of leading cross-functional teams and delivering complex projects on time.
- Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
- Master’s degree in Computer Science, Artificial Intelligence, or a related field is preferred.
- Experience with ethical AI practices and bias mitigation in machine learning models.