Co-create solutions with business and technical stakeholders through workshops, rapid iterations, and hands-on delivery Locate, qualify, and secure access to data required for each use case, working directly with the Data Engineer Translate use cases into production-ready Gen AI and agentic AI solutions, including RAG architectures, intelligent assistants, and AI-enabled workflows Prototype, test, deploy, monitor, and improve solutions in real client environments using feedback from users and domain experts Collaborate with Data Engineers, Software Engineers, Foundations Architects, Governance experts, Business Value Advisors, and Service Delivery Managers Balance speed, quality, cost, security, and maintainability while making technical and delivery trade-offs Define success criteria including adoption, performance, reliability, risk, cost, and measurable business value Ensure solutions are documented, governed, and transferable so clients can operate them with confidence Turn successful delivery into reusable patterns, accelerators, and building blocks for future engagements Requirements Typically 5-10 years of relevant experience in AI Engineering, Machine Learning, Software Engineering, Data Engineering, or technical consulting Hands-on experience delivering AI, Gen AI, or software solutions into production Experience working directly with clients or in complex stakeholder environments Evidence of turning complex use cases into adopted measurable solutions A degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field,



or equivalent practical experience Robust Python development skills API integration experience and solid software-engineering practices Hands-on experience with LLMs and Gen AI, including RAG, embeddings, vector search, AI agents, and agentic workflows Familiarity with AI frameworks such as Lang Chain, Llama Index, Lang Graph, Semantic Kernel, Auto Gen, or similar Experience integrating and deploying AI solutions within enterprise environments and on Azure, AWS, or GCP Working knowledge of Docker, Git, CI/CD, MLOps/LLMOps, monitoring, security, data privacy, governance, and responsible AI principles Core Competencies Demonstrates expertise in delivering AI and Gen AI solutions, with strong capabilities in Python development, API integration, and enterprise deployment on platforms like Azure, AWS, or GCP. Proven ability to collaborate with diverse stakeholders and translate complex use cases into measurable, production-ready solutions. Highest-signal resume keywords AI Engineering Gen AI Solutions Delivery Python Development API Integration Enterprise Deployment ATS Optimization Keywords Hard Skills AI Engineering Machine Learning Software Engineering Data Engineering Python Development API Integration LLMs Gen AI RAG Architectures MLOps Soft Skills Collaboration Stakeholder Engagement Problem Solving Adaptability Communication Industry Keywords Data Privacy Governance Responsible AI AI Frameworks Enterprise Environments Tools & Technologies Azure AWS GCP Docker Git CI/CD Lang Chain Llama Index Lang Graph Semantic Kernel

📌 Forward deployed ai engineer (Montreal)
🏢 Jobtailor
📍 Montreal