• Design, build, and maintain ML Ops pipelines supporting model training, validation, and deployment across AWS environments. • Implement automation for model packaging, testing, deployment, and monitoring using CI/CD best practices. • Collaborate with data engineers and data scientists to operationalize ML workloads within the data lakehouse ecosystem. • Develop and maintain integrations between data ingestion, feature stores, and model repositories. • Apply infrastructure-as-code (Terraform, AWS CDK, CloudFormation) to automate ML pipeline infrastructure. • Implement and manage model versioning, reproducibility, and lineage tracking using tools such as MLflow or SageMaker Model Registry. • Define and automate monitoring, alerting, and retraining strategies for deployed models. • Ensure all ML infrastructure and pipelines meet enterprise security, compliance, and governance standards. • Participate in code reviews, knowledge sharing, and continuous improvement of ML Ops practices. • Mentor junior engineers and contribute to documentation, standards, and best practices for ML Ops across teams.

• Provide strategic and technical leadership to the ML team, ensuring alignment with best practices and industry standards. • Recruit, hire, and onboard top talent, including data scientists and machine learning engineers, to strengthen the team. • Offer mentorship and guidance, helping team members develop their technical skills and advance their careers. • Foster a positive, inclusive, and innovative team culture that promotes collaboration and knowledge-sharing. • Conduct regular performance reviews, set clear objectives, and provide constructive feedback to drive continuous improvement. • Oversee and manage complex ML projects from initial concept to successful deployment. • Ensure the delivery of high-quality outputs on time and within budget, maintaining rigorous standards throughout the project lifecycle. • Collaborate with Delivery Managers to define project scope, timelines, resource allocation, and key milestones. • Proactively track project progress, identify potential risks, and implement effective mitigation strategies to keep projects on track. • Build and maintain strong, trusted relationships with clients, gaining a deep understanding of their business objectives and challenges. • Communicate complex technical concepts clearly and effectively to both technical and non-technical client stakeholders. • Present project findings, insights, and actionable recommendations in a compelling and client-friendly manner, ensuring alignment with their goals.

• Implement and productionize ML solutions (supervised/unsupervised, NLP, deep learning) with robust data preprocessing, feature engineering, and evaluation pipelines • Support model selection, training, validation, optimization, and calibration, ensuring reliability, fairness, and performance at scale • Establish MLOps workflows (CI/CD for ML, experiment tracking, model registry, reproducible builds and deployments) • Implement model monitoring (drift, data/feature quality, bias, and business KPIs), alerting, and automated rollback to keep systems safe and responsive • Design high-quality data pipelines (ingest, transform, validate) across structured and unstructured sources; enforce data contracts and lineage • Partner with analytics teams to make datasets discoverable, documented, and performant for iterative model development • Contribute to coding standards, code reviews, and knowledge sharing; mentor engineers and data scientists • Work in Agile teams; drive iterative delivery, joint problem-solving, and continuous improvement • Engage closely with project managers, technical leads, client representatives, and cross-functional teams to provide timely updates, resolve issues, and ensure alignment with technical goals • Translate technical specifications into code and design documents

• Architect, implement, and maintain scalable data pipelines and feature stores for batch and real-time workloads. • Build reproducible ML training, evaluation, and inference workflows using modern orchestration and MLOps tooling. • Integrate event streams from Twilio products (e.g., Messaging, Voice, Segment) into unified, analytics-ready datasets. • Monitor, test, and improve data quality, model performance, latency, and cost. • Partner with product, data science, and security teams to ship resilient, compliant services. • Automate deployment with CI/CD, infrastructure-as-code, and container orchestration best practices. • Produce clear documentation, dashboards, and runbooks; share knowledge through code reviews and brown-bag sessions. • Embrace Twilio’s “We are Builders” values by taking ownership of problems and driving them to completion.