Gen AI Engineering and Scaled AI Transformation

Single PositionExplore a Career With UsView All JobsHybridGen AI Engineering and Scaled AI TransformationMISSISSAUGA, Ontario, CanadaApply NowFind out how well you match with this jobUpload your resumeJob descriptionJob ID26979423Date posted07/14/2026DepartmentTechnologyRole Focus: Generative AI Engineering and Scaled AI Transformation for Source to Pay technology group - Hybrid1. Large Language Model (LLM) Strategy & Technical AuthorityActs as a senior technical authority on Large Language Models, including both commercial and open‑source ecosystems (OpenAI, Gemini, Claude, Llama).Leads model selection and deployment strategy, balancing use‑case fit, data sensitivity, cost efficiency, latency, accuracy, and regulatory constraints.Guides decisions on hosted vs. private vs. fine‑tuned models, ensuring optimal trade‑offs between performance, control, and operational risk.Establishes enterprise standards for LLM lifecycle management, including upgrades, regression validation, and decommissioning.2. Hands‑On GenAI Application & Agentic System DesignDemonstrates hands‑on leadership in building GenAI applications using LangChain, LangGraph, LlamaIndex, and Hugging Face, translating experimentation into production systems.Architects agentic and multi‑step workflows, enabling tool‑use, reasoning chains, state management, and orchestration at enterprise scale.Sets reusable reference patterns and accelerators for GenAI adoption across application teams.Ensures solutions are built with enterprise-grade reliability, explainability, and extensibility.3. Retrieval Augmented Generation (RAG) & Enterprise Knowledge EnablementDesigns and delivers robust RAG architectures that ground GenAI outputs in trusted, auditable enterprise data.Leads implementation of vector databases and embedding strategies (pgvector, Pinecone, Weaviate, FAISS), aligned with data access and security models.Applies advanced retrieval techniques including hybrid search, re‑ranking, metadata filtering, and context optimization to improve response accuracy and relevance.Ensures RAG solutions support data lineage, auditability, and regulatory compliance.4. Prompt Engineering, Workflow Optimization & Cost ControlEstablishes prompt engineering and orchestration standards to ensure consistency, maintainability, and quality across GenAI solutions.Optimizes GenAI workflows by actively managing latency, throughput, token cost, and accuracy trade‑offs in production environments.Implements evaluation and experimentation frameworks to continuously improve output quality and business value.Drives disciplined use of caching, batching, fallback models, and token optimization techniques.5. Machine Learning & Model Enablement FoundationsApplies strong grounding in ML/DL fundamentals, enabling informed architectural decisions and credible engagement with data science teams.Leverages PyTorch and TensorFlow for embeddings, training pipelines, and targeted fine‑tuning where business value is clear.Ensures GenAI capabilities integrate seamlessly into the broader ML, data, and MLOps ecosystem.Balances rapid GenAI delivery with long‑term model sustainability and governance.6. Production Deployment, Scalability & Operational ExcellenceLeads deployment of GenAI systems into secure, scalable production environments using Docker, cloud‑native architectures, and hardened APIs.Establishes observability and monitoring for GenAI applications, covering performance, drift, quality, reliability, and failure modes.Ensures GenAI platforms meet enterprise availability, resilience, and disaster recovery expectations.Drives operational readiness, incident management, and ongoing optimization of AI services.7. Software Engineering LeadershipBrings strong hands‑on software engineering credibility, setting standards for Python‑based GenAI services.Leads development of high‑performance AI‑powered APIs using FastAPI and async programming patterns.Champions clean architecture, testability, and security best...