• Design, build, and maintain cloud-native data infrastructure using Terraform for IaC. • Develop and optimize data pipelines leveraging AWS services (Lambda, Fargate, Step Functions, S3, Kinesis, DynamoDB, Aurora, etc.) and Snowflake. • Implement ELT workflows within dbt in partnership with our Analytics Engineering function. • Build and maintain LLM frameworks, ensuring high-quality and cost effective outputs. • Automate infrastructure and pipeline deployments with CI/CD best practices. • Monitor, debug, and improve system performance with strong observability and logging practices. • Partner with cross-functional teams (analytics, data science, product, engineering) to deliver high-quality data solutions. • Mentor teammates and contribute to engineering standards, raising the technical bar across the team.

At ClickUp, we’re not just building software. We’re architecting the future of work! In a world overwhelmed by work sprawl, we saw a better way. That’s why we created the first truly converged AI workspace, unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI, empowering millions of teams to break free from silos, reclaim their time, and unlock new levels of productivity. At ClickUp, you’ll have the opportunity to learn, use, and pioneer AI in ways that shape not only our product, but the future of work itself. Join us and be part of a bold, innovative team that’s redefining what’s possible! 🚀 We are seeking a highly skilled and motivated ML Engineer to join our team. This role sits at the intersection of machine learning, data science, and MLOps, requiring you to own the full lifecycle of ML systems — from feature engineering to model production deployment and monitoring. You will collaborate closely with data scientists, analysts, and data engineering teams to build robust, scalable ML systems that drive impactful business decisions. The Role - Model Development & Deployment: Deploy production-grade machine learning models, ensuring reliability, low latency, and scalability. - MLOps & Infrastructure: Build and maintain end-to-end ML pipelines, including automated training, evaluation, versioning, deployment, and monitoring workflows. - Feature Engineering: Partner with data scientists to design, implement, and optimize feature pipelines that feed into ML models, ensuring data quality and freshness. - Model Performance & Monitoring: Establish monitoring frameworks to track model performance, detect drift, and trigger retraining as needed. - Data Science Enablement: Work alongside data scientists to translate research prototypes into production-ready systems, and create tooling that accelerates experimentation. - Collaboration: Act as a bridge between data science and software engineering teams, ensuring seamless integration of ML models into broader product and platform architectures. - Performance Optimization: Continuously improve model inference speed, pipeline efficiency, and overall system scalability. Qualifications - Experience: 4+ years of experience in ML engineering, data engineering, or a related role, with at least 2 years focused on building and deploying machine learning systems in production. - Technical Skills: - Strong proficiency in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn). - Hands-on experience with MLOps tools and platforms (e.g., MLflow, SageMaker, Kubeflow, Vertex AI). - Solid SQL skills and experience with data warehouses and feature stores. - Experience with big data technologies (e.g., Spark, Hadoop) and streaming frameworks. - Expertise in cloud platforms (e.g., AWS, GCP, Azure) and containerization tools (e.g., Docker, Kubernetes). - Familiarity with CI/CD practices applied to ML workflows. - ML Knowledge: Strong understanding of machine learning algorithms, model evaluation techniques, feature engineering, and experiment tracking. - Soft Skills: Strong problem-solving abilities, excellent communication skills, and a collaborative mindset with the ability to work across technical and non-technical stakeholders. Desirable - Education: Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Engineering, or a related field. Unsure if you meet all the qualifications of this job description but are deeply excited about the role? We hire based on ambition, grit, and a passion for improving the way people work. If you think ClickUp is the company for you, we encourage you to apply! At ClickUp, we assess every candidate based on the potential impact they can have. We hire the best people for the job and support each person’s journey to build their boldest career. Equal Opportunity Employer ClickUp is an Equal Opportunity Employer, and qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin. Privacy Notice ClickUp collects and processes personal data in accordance with applicable data protection laws. - If you are a European Job Applicant, see our privacy policy for further details. - If you are a Philippine Job Applicant, see our privacy policy and our Philippine Data Privacy Notice for further details. Visa Sponsorship Please note we are unable to sponsor or take over sponsorship of an employment visa for roles outside of engineering and product at this time. Sponsorship for engineering and product roles is not guaranteed, but is instead based on the business needs for that specific role at that time. Please reach out to the recruiter with any questions. Fraud Alert ClickUp Talent Acquisition will only initiate contact via an @clickup.com email or through our official careers portal on clickup.com. We will never request fees, payments, or sensitive personal information. Please disregard any offers received outside these channels and report them to support@clickup.com.

• Design and implement a scalable, robust, and consistent data architecture, aligned with business objectives, that supports both operational and analytical needs. • Own and support data pipelines integrating data sourced across multiple systems. Deliver a highly available data platform leveraged by business users across the enterprise. • Partner with engineering teams to integrate AI and machine learning models into the data architecture, enabling predictive analytics, automation, and improved decision-making capabilities. • Work closely with business and project stakeholders to deliver data solutions for a diverse set of problems. • Help ensure security, privacy, and compliance for all data assets and workflows. • Create comprehensive documentation for data processes, structures, and pipelines. Provide training and support to ensure best practices are followed throughout the organization. Design and develop data pipelines for data collection, analysis, and related functions. • Establish and enforce data governance standards, ensuring data consistency, accuracy, and integrity across all systems and departments. • Provide thought leadership for technical architecture design, development, and deployment to support enterprise-wide data needs.

• Plays a leadership role in the analysis, design, development, documentation, implementation and maintenance support of data management platforms for all areas of the Bank through the project life-cycle and across single or multiple systems. • Investigates and trains other staff on identification of program errors, definition of problems, debugging, unit testing and implementation of changes. • Trains junior staff in data engineering concepts, standards, and best practices. • Provides training, assistance, and guidance for maintenance support of existing Bank applications, data management and analytics platforms and related package products as directed. • Interfaces with other data engineering, and data science teams to implement bank initiatives. • Demonstrates technical leadership by staying abreast of emerging data engineering technologies and implementing industry best practices. • Lead developing data solutions, provide subject matter expertise utilizing SQL Server, .net and Microsoft Azure technology stack and other relevant technologies in coordination with Enterprise Architects. • Actively leads process improvement initiatives. • At times, may be responsible to lead process improvement initiatives. • Reports task progress to applicable stakeholders. • Performs other duties as assigned.