Placement Type: E‑Volunteering (Online) Duration: 3–6 months with a flexible start date (Indicate your preferred start date, duration, and weekly availability in your application) Location: Remote (supporting partners in Africa, Latin America, or the Caribbean) Language Requirements: Fluency in English, French, or Spanish, depending on partner location Eligibility: Open to Canadian citizens and permanent residents of Canada only About the E‑Volunteering Placement Build global experience from wherever you are with Cuso International. These E‑Volunteering Placements are designed for final‑year undergraduate students, graduate students, and recent graduates seeking meaningful, skills‑based experience in global development, entirely online. Through E‑Volunteering Placements, Cuso International connects students and recent graduates with partner organizations seeking specific skills and expertise. E‑volunteers may work independently on defined assignments or collaborate virtually with an in-country volunteer, enabling volunteers to engage meaningfully in international development work while remaining based in Canada. We invite you to share your skills and explain how your academic background, education, volunteer experience, or professional experience can support our partners’ priorities. Undergraduate‑Level Skills & Areas of Interest (Final‑year undergraduate students and recent bachelor’s graduates) - Communications & Digital Media (communications, social media, graphic design, content creation) - Research & Knowledge Support (desk research, data collection, summaries, reporting support) - Gender Equality, Youth & Community Development (GESI support, youth engagement, community wellbeing initiatives) - Monitoring & Data Support (MEAL assistance, data analysis, documentation) - Information Technology & Digital Support (IT support, data systems assistance, digital tools) - Program & Administrative Support (coordination, planning assistance, logistics, reporting)   Graduate‑Level Skills & Areas of Interest (Graduate students and recent master’s graduates) - Business Development & Entrepreneurship (enterprise development, youth entrepreneurship, private sector engagement) - Economic Development & Livelihoods (employability, workforce readiness, market‑based development, economic resilience) - Partnership Development & Resource Mobilization (fundraising support, stakeholder engagement, partnership building) - Program & Training Design (TVET, curriculum design, labour‑market alignment, training development) - Monitoring, Evaluation & Impact Measurement (MEAL design, learning frameworks, data analysis) - Digital Systems & Data Strategy (information systems, digital transformation, data‑driven decision‑making) - Environment, Climate Change & Sustainability (climate adaptation, environmental programming, sustainable development) Key Responsibilities Responsibilities are adapted based on academic level, experience, and partner needs. Graduate-level volunteers may take on additional technical, analytical, or strategic responsibilities aligned with their expertise. - Support the delivery of partner‑led projects and initiatives through remote collaboration - Contribute to research, data analysis, reporting, and documentation - Assist with planning, coordination, and virtual meetings or workshops - Support the development of communications and knowledge‑sharing materials, including reports, briefs, presentations, or digital content - Contribute to monitoring, evaluation, and learning activities, including data tracking and reflection - Apply academic knowledge and professional skills to strengthen partner capacity and systems - Collaborate effectively with multicultural teams across time zones - Participate actively in virtual learning, reflection, mentoring, and professional development activities - Uphold Cuso International’s values related to gender equality, inclusion, accountability, and ethical engagement Requirements Applicants must demonstrate relevant academic, volunteer, and/or work experience in one or more of the fields outlined above. Experience may include coursework, internships, placements, volunteer roles, student leadership, or paid employment. What You’ll Gain - International development experience in a remote, professional setting - Enhanced technical, digital, and cross‑cultural collaboration skills - Experience contributing to locally led global development initiatives - Increased understanding of partnership‑based and ethical development approaches - Meaningful contribution to global social impact, without international travel   About Us, the Project and the Partner Cuso International (Cuso) is an international cooperation and development organization based in Canada that works to create social and economic opportunities for equity-deserving groups. Building on over sixty years of impactful work, we address global challenges by tackling the root causes of inequality. Working alongside our partners, we are focused on efforts to improve economic resilience, advance equity and inclusion for all, and deliver progress on climate action. We believe that by sharing skills, we can build better futures. Guided by our values of courage, inclusion, sustainability and accountability, Cuso combines innovative strategies and enduring partnerships to create lasting, meaningful change in communities across Africa, Latin America, the Caribbean, and Canada. Project:  Sharing Canadian Expertise for Inclusive Development and Gender Equality (SHARE)

• Deliver end-to-end ML use cases (recommendation, safety algorithms, etc.). • Ensure production readiness, scalability, and long-term maintainability. • Balance speed of delivery with robustness and reliability. • Define and improve the full ML lifecycle (training, deployment, monitoring, iteration). • Establish KPIs and monitoring standards to track model performance over time. • Ensure continuous alignment with product and safety objectives. • Contribute to the “ML as a Platform” strategy (tools, workflows, reusable components). • Define scalable standards for ML development across teams. • Enable self-service ML capabilities. • Take ownership of legacy models and realign them with current business needs. • Improve, retrain, and integrate them into modern pipelines. • Define standards for LLM usage (moderation, recommendation). • Explore and implement advanced ML approaches where relevant. • Partner with Data Engineering, MLOps, Backend Platform, and Product teams. • Act as a bridge between ML, platform, and business stakeholders. • Bring technical leadership and structure to ML practices across the organization.

• Design and deliver ML pipelines from experimentation to production; • Build and optimize models — supervised, unsupervised, and generative AI; • Write clean, tested, modular Python code; • Deploy and monitor models; track performance and prevent drift; • Contribute to LLM applications: RAG systems and agent workflows; • Use AI coding tools on every task to move faster and write better code; • Mentor junior engineers and give actionable code review feedback; • Work closely with DevOps, Data Engineering, and Solutions Architects; • Share knowledge through docs, presentations, or internal workshops; • Stay current with ML research, GenAI, and agentic frameworks; • Propose process improvements and reusable ML accelerators; • Participate in architectural design and trade-off discussions.

Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses. As an ML Engineer, you’ll be provided with all opportunities for development and growth. Let's work together to build a better future for everyone! Responsibilities: - Technical Delivery (60%) - Design and implement end-to-end ML solutions from experimentation to production; - Build scalable ML pipelines and infrastructure; - Optimize model performance, efficiency, and reliability; - Write clean, maintainable, production-quality code; - Conduct rigorous experimentation and model evaluation; - Troubleshoot and resolve complex technical challenges. - Collaboration and Contribution (25%); - Mentor junior and mid-level ML engineers; - Conduct code reviews and provide constructive feedback; - Share knowledge through documentation, presentations, and workshops; - Collaborate with cross-functional teams (DevOps, Data Engineering, SAs); - Contribute to internal ML practice development. - Innovation and Growth (15%) - Stay current with ML research and emerging technologies; - Propose improvements to existing solutions and processes; - Contribute to the development of reusable ML accelerators; - Participate in technical discussions and architectural decisions. Requirements: - Machine Learning Core - ML Fundamentals: supervised, unsupervised, and reinforcement learning; - Model Development: feature engineering, model training, evaluation, hyperparameter tuning, and validation; - ML Frameworks: classical ML libraries, TensorFlow, PyTorch, or similar frameworks; - Deep Learning: CNNs, RNNs, Transformers. - LLMs and Generative AI - LLM Applications: Experience building production LLM-based applications; - Prompt Engineering: Ability to design effective prompts and chain-of-thought strategies; - RAG Systems: Experience building retrieval-augmented generation architectures; - Vector Databases: Familiarity with embedding models and vector search; - LLM Evaluation: Experience with evaluation metrics and techniques for LLM outputs. - Data and Programming - Python: Advanced proficiency in Python for ML applications; - Data Manipulation: Expert with pandas, numpy, and data processing libraries; - SQL: Ability to work with structured data and databases; - Data Pipelines: Experience building ETL/ELT pipelines - Big Data: Experience with Spark or similar distributed computing frameworks. - MLOps and Production - Model Deployment: Experience deploying ML models to production environments; - Containerization: Proficiency with Docker and container orchestration; - CI/CD: Understanding of continuous integration and deployment for ML; - Monitoring: Experience with model monitoring and observability; - Experiment Tracking: Familiarity with MLflow, Weights and Biases, or similar tools. - Cloud and Infrastructure - AWS Services: Strong experience with AWS ML services (SageMaker, Lambda, etc.); -GCP Expertise: Advanced knowledge of GCP ML and data services; - Cloud Architecture: Understanding of cloud-native ML architectures; - - Infrastructure as Code: Experience with Terraform, CloudFormation, or similar. Will be a plus: - Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda); - Practical experience with deep learning models; - Experience with taxonomies or ontologies; - Practical experience with machine learning pipelines to orchestrate complicated workflows; - Practical experience with Spark/Dask, Great Expectations. What We Offer: - Long-term B2B collaboration; - Fully remote setup; - A budget for your medical insurance; - Paid sick leave, vacation, public holidays; - Continuous learning support, including unlimited AWS certification sponsorship. Interview stages: - Recruitment Interview; - Tech interview; - HR Interview; - HM Interview.