• Design, develop, and deploy multi-agent AI systems for reasoning, planning, and task execution. • Implement RAG (Retrieval-Augmented Generation) pipelines, using orchestration tools and frameworks (LangChain, LangGraph, Azure AI Foundry). • Optimize model serving for low-latency, high-throughput inference using frameworks like KServe, Triton Inference Server, or Ray Serve. • Build observability and evaluation frameworks to monitor agent reasoning, success rates, and failure cases. • Collaborate with ML and data engineers to integrate structured and unstructured data sources into agent workflows. • Apply security and alignment techniques (guardrails, prompt injection prevention, red-teaming) to ensure robust behavior. • Work in a CI/CD environment (Azure DevOps, GitHub Actions, ArgoCD) for rapid iteration and reliable deployment. • Participate in architectural decisions involving distributed systems, GPU usage, caching strategies, and memory management.

• Design and build AI-powered applications using LLMs, RAG architectures, and other applied AI systems • Develop and maintain backend services that support AI platforms and integrate seamlessly with cloud infrastructure • Build and maintain integrations across various applications to extend AI capabilities • Deploy, optimize, monitor, and troubleshoot AI solutions on AWS, Azure, and/or GCP • Implement containerization and orchestration strategies using Docker and Kubernetes • Ensure AI systems meet performance, scalability, and reliability standards in production • Design scalable AI architectures that translate business requirements into technical solutions • Collaborate with cross-functional teams throughout the full development lifecycle • Contribute to code reviews, technical documentation, and engineering best practices

Role Description We are seeking ML/AI Engineers to contribute to major projects. The ML / AI Engineer design, build, deploy, and operate production-grade machine learning and generative AI systems. This role owns the end-to-end ML lifecycle, ensuring models and AI services are scalable, reliable, secure, and deliver measurable business value. The role will be remote. Why Join Levio? - Work on complex, high impact digital transformation projects - Collaborate with experienced, multidisciplinary teams - Continuously develop your technical and professional expertise - Enjoy flexibility, autonomy, and a strong people first culture - Be part of an organization that values diversity, inclusion, and innovation Role and Responsibilities - Design, implement, and productionize machine learning and generative AI models - Build training, validation, and inference pipelines for ML and LLM-based solutions - Implement feature engineering, embeddings, and model versioning best practices - Support LLM-based systems, including RAG architectures and inference optimization - Implement CI/CD for ML and LLM workflows, including automated deployment and rollback - Monitor AI systems for performance, drift, bias, and reliability in production - Optimize compute usage, latency, and operational costs across AI workloads - Ensure AI systems comply with security, privacy, and responsible-AI standards - Collaborate with AI Architects, Developers, and Data Engineers across delivery teams - Mentor junior ML/AI engineers and contribute to engineering best practices Qualifications - Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field - 6+ years of experience in engineering roles, with 3+ years in AI/ML positions - Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn) - Hands-on experience deploying and operating ML models in production - Experience with cloud ML platforms, especially AWS SageMaker - Strong understanding of data pipelines, model lifecycle management, and monitoring Compensation (Ontario) The salary range provided reflects a good faith estimate based on factors such as experience, technical expertise, location, and relevant certifications. Final compensation will be determined according to the specific circumstances of each candidate. Estimated salary range: $110,000 to $150,000 per year. Benefits - 4 weeks of cumulative vacation starting from day one - Flexible working hours - Professional Development Allowance (PDA) for training, computer equipment, and physical activities - Training tailored to your areas of expertise - Registered Retirement Savings Plan (RRSP) with employer contribution up to 3% of gross salary - Modular group insurance plan - Public transportation or parking reimbursement when required - Referral bonuses - 11 statutory holidays - Personal days - An active social life (5to7 events, social club, healthy snacks, coffee, and more) Position Details - Employment type: Full time, permanent Employment Equity Levio subscribes to the principle of employment equity and applies an equal access employment program for women, Indigenous peoples, visible minorities, ethnic minorities, and persons with disabilities. We value diversity and inclusion and are committed to creating a healthy, accessible, and rewarding work environment that highlights the unique contributions of our employees. Accommodations are available upon request for candidates participating in all aspects of the selection process.

• System Design & Development: Architect and develop an agentic AI platform. Use this platform to implement the initial agentic workflows capable of automating complex processes in areas including Finance, HR, Legal, Customer Support, and Operations. Mentor more junior agentic AI developers to further expand such flows. • Tool Integration: Work with a variety of AI tools and platforms (e.g., Claude, AWS QuickSight, OpenAI, n8n, LangChain, etc.) to build seamless, integrated solutions. • End-to-End Automation: Develop and deploy end-to-end solutions that include data ingestion, processing, decision-making, and action execution. • Custom AI Models: Fine-tune and customize AI models to meet specific business requirements, ensuring high accuracy and reliability. • Collaboration: Collaborate with cross-functional teams, including data scientists, engineers, and business stakeholders, to understand requirements and deliver impactful solutions. • Performance Optimization: Continuously monitor, evaluate, and optimize the performance of agentic systems to ensure scalability, efficiency, and robustness. • Documentation & Reporting: Maintain clear documentation of system architecture, workflows, and processes. Provide regular updates and reports to stakeholders on system performance and outcomes. • Innovation: Stay updated on the latest advancements in AI, machine learning, and automation technologies, and proactively identify opportunities to enhance existing systems.