AI Engineer

This is a remote position. Build Scalable Machine Learning Solutions That Power Innovation as An AI Engineer This is a remote position. Full-Time | 40 Hours per week Open and negotiable based on skill and experience 8:00 AM to -5:00 PM PHT, Monday - Friday About the Role: As an AI Engineer, you'll play a key role in transforming complex data challenges into scalable, high-performing AI applications that fuel business growth and product innovation. You'll work cross-functionally with data scientists, software engineers, and product stakeholders to create models that are not only accurate but also production-ready. Why You'll Love This Role: You'll work on diverse, high-impact AI initiatives using the latest tools and techniques. Join a forward-thinking team where experimentation, learning, and continuous improvement are valued. Enjoy flexibility and autonomy in a 100% remote setting while growing professionally in a cutting-edge field. Key Responsibilities: 1. Design and Build Scalable AI Solutions Conceptualise, design, and develop machine learning models and AI algorithms tailored to business challenges, including NLP, computer vision, predictive analytics, recommendation systems, and anomaly detection. Translate product requirements and research insights into effective and scalable AI models. 2. Train, Deploy, and Operationalize Models Implement & Train: Implement ML algorithms and tools; conduct experiments, train, and evaluate models using appropriate frameworks and computational resources. Deploy trained models into production environments using MLOps best practices to ensure performance, scalability, and reliability. Integrate AI/ML capabilities into existing systems and applications, collaborating closely with software engineering teams. Develop and manage CI/CD pipelines for ML workflows, utilizing tools like Docker and Kubernetes, and cloud platforms such as AWS, Azure, or GCP for deployment and scaling. 3. Monitor, Improve, and Innovate AI Performance Build monitoring systems to track model performance, data drift, and system health in production environments. Continuously optimize and retrain models based on real-time metrics, user feedback, and new data inputs. Research and evaluate emerging AI techniques, tools, and publications to prototype innovative approaches and improve model effectiveness. 4. Manage Data and Drive Collaborative Development Collaborate with data engineers and scientists to define data needs and preprocess, clean, and transform large datasets for effective model training and validation. Conduct feature engineering to enhance model accuracy and robustness. Clearly document methodologies, models, code, and results to ensure reproducibility and facilitate knowledge sharing across teams. Work closely within a cross-functional team environment to align AI initiatives with broader product and business goals. Performance Milestones: First 30 Days: Understand the AI architecture and contribute to initial model ideation or prototyping. In 60 Days: Successfully deploy at least one ML model to a staging or production environment with measurable performance outputs. In 90 Days: Deliver an optimized model with improved accuracy or efficiency, supported by production-level monitoring and alerting mechanisms. Key Performance Objectives (KPOs): Deploy at least one production-ready AI model with validated impact on key business or product metrics within the first 90 days. Maintain model uptime and reliability standards, ensuring issues such as model drift or performance degradation are identified and resolved proactively. Collaborate effectively with cross-functional teams to ensure AI components are integrated seamlessly and aligned with product requirements. Deliver clear, well-documented models and methodologies that support reproducibility, scalability, and team knowledge transfer. Continuously identify and prototype at least one new AI approach or model improvement per quarter based on research or performance analysis.

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