Infotech is seeking a Principal ML Architect to serve as a technical leader for all AI/ML initiatives, build the ML function, and mentor a growing team of AI/ML engineers. The role aims to build, scale, and deploy mission-critical AI/ML systems with measurable business impact, transitioning projects from research to production-grade systems and driving the company's AI strategy.
Requirements
- 10+ years of experience across AI and Machine Learning.
- Strong understanding of machine learning, natural language processing, and data science.
- Experience transitioning projects and teams from research/prototype to production-grade systems.
- History of pioneering effective ML approaches or architectures.
- Proven ability to build and deploy ML systems at scale, supporting 10,000+ users/devices.
- Track record of developing mission-critical ML applications with measurable business impact.
- Deep learning, natural language processing, predictive modeling; Data engineering, MLOps, and ML lifecycle automation; Cloud-based ML infrastructure (AWS, GCP, Azure); Languages: Python, SQL, and modern AI/ML frameworks; Responsible AI practices, bias mitigation, and regulatory compliance
Responsibilities
- Define and lead the enterprise AI and ML architecture
- Pioneer effective ML approaches and architectures
- Lead the end-to-end engineering of AI/ML solutions
- Oversee the efficient and reliable operationalization of models in production environments
- Establish and enforce rigorous standards for model evaluation, validation, and governance
- Build and deploy ML systems at scale
- Transitioning projects and teams from research/prototype to production-grade systems
Other
- Serve as champion and technical leader across all AI/ML initiatives.
- Serve as a mentor to a growing team of AI/ML engineers.
- Serve as a technical authority and thought leader for the company’s AI strategy.
- Mentor a world-class team of AI/ML engineers and researchers focused on rapid iteration and delivery.
- Collaborate seamlessly with engineering, product, and business teams to identify opportunities for AI-driven solutions and translate them into technical requirements and deployed systems, deconstructing complex business problems into manageable architectural components.
