Co-create solutions with business and technical stakeholders through workshops, rapid iterations, and hands-on deliveryLocate, qualify, and secure access to data required for each use case, working directly with the Data EngineerTranslate use cases into production-ready GenAI and agentic AI solutions, including RAG architectures, intelligent assistants, and AI-enabled workflowsPrototype, test, deploy, monitor, and improve solutions in real client environments using feedback from users and domain expertsCollaborate with Data Engineers, Software Engineers, Foundations Architects, Governance experts, Business Value Advisors, and Service Delivery ManagersBalance speed, quality, cost, security, and maintainability while making technical and delivery trade-offsDefine success criteria including adoption, performance, reliability, risk, cost, and measurable business valueEnsure solutions are documented, governed, and transferable so clients can operate them with confidenceTurn successful delivery into reusable patterns, accelerators, and building blocks for future engagementsRequirementsTypically 5-10 years of relevant experience in AI Engineering, Machine Learning, Software Engineering, Data Engineering, or technical consultingHands-on experience delivering AI, GenAI, or software solutions into productionExperience working directly with clients or in complex stakeholder environmentsEvidence of turning complex use cases into adopted measurable solutionsA degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field,



or equivalent practical experienceStrong Python development skillsAPI integration experience and solid software-engineering practicesHands-on experience with LLMs and GenAI, including RAG, embeddings, vector search, AI agents, and agentic workflowsFamiliarity with AI frameworks such as LangChain, LlamaIndex, LangGraph, Semantic Kernel, AutoGen, or similarExperience integrating and deploying AI solutions within enterprise environments and on Azure, AWS, or GCPWorking knowledge of Docker, Git, CI/CD, MLOps/LLMOps, monitoring, security, data privacy, governance, and responsible AI principlesCore CompetenciesDemonstrates expertise in delivering AI and GenAI solutions, with robust 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 keywordsAI EngineeringGenAI Solutions DeliveryPython DevelopmentAPI IntegrationEnterprise DeploymentATS Optimization KeywordsHard SkillsAI EngineeringMachine LearningSoftware EngineeringData EngineeringPython DevelopmentAPI IntegrationLLMsGenAIRAG ArchitecturesMLOpsSoft SkillsCollaborationStakeholder EngagementProblem SolvingAdaptabilityCommunicationIndustry KeywordsData PrivacyGovernanceResponsible AIAI FrameworksEnterprise EnvironmentsTools & TechnologiesAzureAWSGCPDockerGitCI/CDLangChainLlamaIndexLangGraphSemantic Kernel #J-18808-Ljbffr

📌 Forward Deployed Ai Engineer (Montreal)
🏢 Jobtailor
📍 Montreal