Senior/Middle AI Engineer

General InformationAs a Mid- Senior Level AI Engineer, you will build upon foundational knowledge to independently design, develop, and maintain AI systems. You are adept at leveraging machine learning algorithms and deep learning frameworks, capable of taking a specific project, breaking it down into necessary steps, and executing them autonomously. You serve as a crucial bridge between high-level concepts and detailed implementation, ensuring the successful execution of AI initiatives. Key ResponsibilitiesWork closely as a project team to lead in the entire lifecycle of data science projects, from gathering business requirements to solution delivery, and deliver project outcomes to internal stakeholders or external customers.Engages with clients to understand business needs, present findings, and align technical work with stakeholder goals.Owns planning of assigned modules, contributes to task breakdown, estimation, and milestone alignment, and supports backlog grooming discussions.Leads development of a complete module or sub-system (e.g., full model training pipeline or certain solution direction). Can propose solution design, test, and iterate independently. Coordinates with project lead to align with project needs.Provides structured feedback to junior members, supports onboarding, and contributes to skill-building within the project team.Proactive in contributing to the growth of internal capabilities (e.g., training, knowledge-sharing, framework design).Designing and implementing AI solutions for customer use cases, often leveraging core products and frameworks specific to the company (e.g., Google's TensorFlow, DataFlow, and Vertex AI).Developing scalable AI systems and designing/building algorithms that automate predictive data models.Creating and managing the AI development and production infrastructure, including automating AI infrastructures for data science teams.Building AI models from scratch and transforming machine learning models into APIs that can be seamlessly integrated with other applications.Leading software engineering efforts for AI systems, which involves writing production-ready code optimized for existing and upcoming architectures (e.g., Intel architectures).Participating in all phases of AI solutions development, from requirements definition and design to development, deployment, integration, maintenance, performance tuning, and monitoring.Collaborating extensively with cross-functional teams, including data scientists, software engineers, and product managers, to define project requirements, ensure seamless progress, and facilitate AI adoption and best practices.Driving scalability improvements and introducing new capabilities in machine learning platforms, often working across the full stack on tools and infrastructure that empower machine learning teams.Conducting statistical analysis and interpreting results to guide and optimize organizational decision-making processes.