Role Description Zipdev is helping its client in building and evolving software that supports modern clinical and operational workflows. The product surface includes Windows desktop applications and connected services, with a strong emphasis on reliability, maintainability, and customer experience. As a Full‑Stack Engineer, you will deliver end-to-end capabilities across the Windows client, service/business logic layers, and the database. Your primary stack will include: - C++ (MFC) for desktop UI and application behavior - C# for services and shared business logic - MS SQL for data persistence Qualifications - Strong experience developing Windows desktop applications using C++ and MFC - Strong experience developing with C# (services, libraries, or application layers) - Solid relational database skills with MS SQL (schema design, query awareness, performance basics) - Proven ability to debug complex issues across layers (UI ↔ business logic ↔ database) and drive fixes to completion - Familiarity with Git-based workflows (pull requests, code reviews) and maintaining engineering documentation - Hands-on experience using AI coding assistants as part of daily development, with a clear understanding of the Human → AI → Human review loop and human accountability - Strong understanding that Protected Health Information (PHI) must never appear in code, logs, tests, commits, or error messages; comfort working with synthetic/mock data - Awareness of secure coding practices (no hardcoded credentials, least-privilege access patterns, safe error handling) - Must be located in LATAM and have high English proficiency (C1+) Requirements - Design, build, and maintain features across the full stack (C++/MFC desktop → C# services/business logic → MS SQL) - Own features from requirements through implementation, test readiness, release, and production support - Collaborate with QA and product partners to refine acceptance criteria, validate behavior, and ensure release readiness in an Agile cadence - Participate in AI-augmented development workflows, reviewing and validating AI-generated code against acceptance criteria - Follow Git best practices: feature branches, pull requests for all changes, and clear commit hygiene; proactively update documentation when behavior or architecture changes - Design, build, and maintain Windows workflows using C++ and MFC - Debug and remediate defects across UI, threading, memory/resource management, and performance hotspots - Modernize and refactor legacy areas to improve maintainability, testability, and stability while preserving user workflows - Implement defensive coding patterns and robust error handling suitable for regulated environments - Develop and maintain C# components and service layers that support desktop workflows and integrations - Design and consume APIs or internal contracts as needed to connect client and service components - Implement secure authentication/authorization patterns appropriate for enterprise and regulated contexts - Create and maintain automated checks and diagnostics to support supportability and observability - Design and evolve MS SQL schemas with emphasis on clarity, performance, and data integrity - Write and optimize queries, stored procedures, and migration scripts where appropriate - Collaborate with QA and Engineering to validate data correctness and enable reliable test data setup - Contribute to troubleshooting production issues through data analysis and root cause investigation - Demonstrate knowledge of QA processes in an Agile environment, including acceptance criteria, defect triage, regression awareness, and collaboration with QA roles - Contribute to test strategy (unit/integration as appropriate), and ensure features meet functional, security, and reliability expectations - Maintain a disciplined approach to compliance and secure engineering practices Benefits - Work remotely Monday - Friday, 40 hours a week (no weekends) - Health Care Reimbursement - Active Lifestyle Reimbursement - Quarterly Home Office Reimbursement - Did we mention it's REMOTE?!!

• Developing and maintaining services for control plane including API endpoints, configuration management, and platform workflows • Building customer-facing features that enable users to configure, manage, and interact with inference platform resources • Writing well-tested, well-documented code and participating in code reviews to maintain engineering standards • Collaborating with engineering and product teams to translate requirements into reliable platform capabilities • Contributing to platform reliability through monitoring, debugging, and operational support of control plane services

Job Title: Full-Stack AI Engineer Position Type: Full-Time, Remote Working Hours: U.S. client business hours (with flexibility for model deployments, experimentation cycles, and sprint schedules) About the Role: Our client is seeking a Full-Stack AI Engineer to design, build, and deploy AI-powered applications. This role requires bridging software engineering with applied machine learning, ensuring that models are integrated into production systems that are scalable, reliable, and user-friendly. The Full-Stack AI Engineer combines back-end services, front-end interfaces, and machine learning pipelines to deliver practical, business-driven AI solutions. Responsibilities: AI Model Integration: - Deploy pre-trained and fine-tuned ML/LLM models (OpenAI, Hugging Face, TensorFlow, PyTorch). - Wrap models in APIs (FastAPI, Flask, Node.js) for scalable inference. - Implement vector search integrations (Pinecone, Weaviate, FAISS) for retrieval-augmented generation (RAG). Data Engineering & Pipelines: - Build ETL pipelines for ingesting, cleaning, and transforming text, image, or structured data. - Automate data labeling, preprocessing, and versioning with Airflow, Prefect, or Dagster. - Store and manage datasets in cloud warehouses (Snowflake, BigQuery, Redshift). Application Development (Full-Stack): - Build front-end UIs in React, Next.js, or Vue to surface AI-powered features (chatbots, dashboards, analytics). - Design back-end services and microservices to connect models to business logic. - Ensure responsive, intuitive, and secure interfaces for end users. Infrastructure & Deployment: - Containerize ML services with Docker and deploy to Kubernetes clusters. - Automate CI/CD pipelines for model updates and application releases. - Monitor latency, cost, and model drift with MLflow, Weights & Biases, or custom dashboards. Security & Compliance: - Ensure AI systems comply with data privacy standards (GDPR, HIPAA, SOC 2). - Implement rate limiting, access control, and secure API endpoints. Collaboration & Iteration: - Work with data scientists to productionize prototypes. - Partner with product teams to scope AI features aligned with business needs. - Document systems for reproducibility and knowledge transfer. What Makes You a Perfect Fit: - Strong coder with a foundation in both full-stack development and applied ML/AI. - Comfortable building prototypes and scaling them to production-grade systems. - Analytical problem solver who balances performance, cost, and usability. - Curious and adaptable, staying current with emerging AI/LLM tools and frameworks. Required Experience & Skills (Minimum): - 3+ years in software engineering with exposure to AI/ML. - Proficiency in Python (PyTorch, TensorFlow) and JavaScript/TypeScript (React, Node.js). - Experience deploying ML models into production systems. - Strong SQL and experience with cloud data warehouses. Ideal Experience & Skills: - Built and scaled AI-powered SaaS products. - Experience with LLM fine-tuning, embeddings, and RAG pipelines. - Knowledge of MLOps practices (Kubeflow, MLflow, Vertex AI, SageMaker). - Familiarity with microservices, serverless architectures, and cost-optimized inference. What Does a Typical Day Look Like? A Full-Stack AI Engineer’s day revolves around connecting models to real-world applications. You will: - Review and refine model APIs, testing latency and accuracy. - Write front-end code to surface AI features in user-friendly interfaces. - Maintain pipelines that clean and prepare new datasets for training or fine-tuning. - Deploy updates through CI/CD pipelines, monitoring cost and performance post-release. - Collaborate with product and data science teams to prioritize AI features that solve real user problems. - Document workflows and results so solutions are repeatable and scalable. In essence: you ensure AI moves from prototype to production — reliable, compliant, and impactful. Key Metrics for Success (KPIs): - Successful deployment of AI features to production on schedule. - Application uptime ≥ 99.9% and inference latency < 500ms for key endpoints. - Reduction in manual workflows replaced by AI features. - Model performance tracked and stable (accuracy, drift, false positives/negatives). - Positive user adoption and satisfaction of AI-driven features. Interview Process: - Initial Phone Screen - Video Interview with Pavago Recruiter - Technical Assessment (e.g., deploy a small ML model with API endpoints and basic front-end integration) - Client Interview(s) with Engineering Team - Offer & Background Verification

• Design and implement insights and analytics dashboards for the CTV self-service platform, providing users with actionable data to enhance their campaigns. • Develop both frontend (70%) and backend (30%) services using React, Next.js, and TypeScript, ensuring seamless integration between components. • Build robust, scalable APIs using tRPC to serve analytics data to the frontend application. • Create efficient pages using server-side rendering (SSR) or static site generation (SSG) via Next.js for performance-optimized analytics displays. • Collaborate with designers to implement responsive and visually appealing user interfaces using Tailwind CSS, ensuring a smooth, intuitive experience for the platform’s users. • Optimize the full stack to ensure quick load times, low-latency data fetching, and smooth user experiences, especially for data-heavy visualizations and analytics pages. • Deploy and manage the platform using Vercel, taking advantage of its serverless functions for backend processes and ensuring efficient deployments and scaling. • Participate in code reviews, pair programming, and knowledge-sharing sessions to ensure high-quality, maintainable code. • Implement unit and end-to-end testing for both frontend and backend components to ensure reliability and reduce bugs in production.