Role Description In collaboration with the Ministries of Education of Chiapas, Baja California, and Chihuahua, this consultancy will design an Institutionalization Roadmap for the Akelius Learning App programme to enable its sustainable and scalable adoption, aligned with formal curricula and with state and federal education priorities. Key Deliverables: - Situational analysis of the prioritized states - State-level Institutionalization Roadmap proposals (Chiapas, Baja California, and Chihuahua) - Proposal for a teacher training model, including a monitoring and evaluation framework - Consolidated final report Qualifications - Bachelor’s Degree in Education, Public Policy, Social Sciences, or related fields. - At least five (5) years of relevant professional experience in one or more of the following areas: - Education management - Learning technologies - Design of education policies or programmes - Engagement and coordination with state-level education systems - Fluency in Spanish - Fluency in English Requirements - Institutional analysis - Design of implementation or scalability roadmaps - Teacher training and professional development processes - Technical report writing Benefits - Paid parental leave - Breastfeeding breaks - Reasonable accommodation for persons with disabilities Company Description UNICEF works in over 190 countries and territories to save children’s lives, defend their rights, and help them fulfill their potential, from early childhood through adolescence. At UNICEF, we are committed, passionate, and proud of what we do for as long as we are needed. Promoting the rights of every child is not just a job – it is a calling.

• Ship models into production • Debug production pipelines at client sites • Build new ML features ranging from classical ML, computer vision and LLMs • Work hands-on with GPU servers & multi-camera systems • Collaborate with customer surveillance teams and distribution partners

• Design and implement data-driven systems that enhance decision-making across dealer workflows • Develop predictive or optimization logic using structured dealership data • Translate business objectives into scalable intelligent services • Improve consistency, accuracy, and efficiency in operational processes • Ensure solutions are explainable, reliable, and aligned with enterprise and regulatory expectations • Build systems that extract insights from structured dealership data (CRM, DMS, transactional records) • Implement knowledge retrieval or search capabilities across policies and documentation where appropriate • Collaborate with product teams to surface actionable intelligence within dashboards and workflows • Design and deploy containerized AI services within AWS (ECS/Fargate) • Integrate intelligent services into existing APIs and application workflows • Monitor system performance, reliability, and integrity • Ensure data security, auditability, and compliance alignment

Role Description Our client is building vision agents for large venues such as hotels and casinos— powering real-time video analytics and intelligent surveillance across hundreds of camera streams. Our systems run on-prem in some of the largest resorts in Las Vegas, and many more in the pipeline. They’re a highly technical team shipping deep tech into one of the most operationally demanding and dynamic environments. We’re looking for a Forward Deployed ML Engineer who blends strong technical ML/CV ability with comfort deploying systems in the field. You will own our real-time vision pipelines end-to-end and be the technical face of the client's inside casinos. This role is not a back-office research job. - Ship models into production - Debug production pipelines at client sites - Build new ML features ranging from classical ML, computer vision and LLMs - Work hands-on with GPU servers & multi-camera systems - Collaborate with customer surveillance teams and distribution partners If you love solving real-world problems in messy environments, this is your role. Qualifications - 2-3 years of experience in machine learning with strong knowledge about not just deep learning but also classical ML - Strong skills in Linux, Docker, and shipping models as services - Comfortable working in live production environments with minimal supervision - A startup mindset — resourceful, adaptable, and excited to work across ML, backend, and DevOps boundaries Requirements - Train, tune, and update/deploy deep learning models at client sites - Maintain low-latency inference pipelines on-premise using PyTorch, ONNX, and TensorRT and Triton - Build training data processing pipelines, QA/QC labeling and coordinate work with our labeling teams - Work closely with customers and with the product manager to experiment and ship new features Benefits - Work remotely Monday - Friday, 40 hours a week (no weekends) - Vacation: 10 business days a year - Holidays: 5 National Holidays a year - Company Holidays: 5 Company Holidays a year (Christmas Eve, Christmas Day, New Year's Eve, New Year's Day, Zipdev Day) - Parental Leave - Health Care Reimbursement - Active Lifestyle Reimbursement - Quarterly Home Office Reimbursement - Payroll Deduction Purchase Plans - Longevity Bonus - Continuous Learning Bonus - Access to Training and Professional Development Platforms