## Gen AI Engineering and Scaled AI TransformationApplyremote type:

Hybridlocations:

Mississauga Ontario Canadatime type:

Full timeposted on:

Posted Todaytime left to apply:

End Date: July 24, 2026 (6 days left to apply)job requisition id:

26979423Role Focus: Generative AI Engineering and Scaled AI Transformation for Source to Pay technology group
- Hybrid1. Large Language Model (LLM) Strategy & Technical Authority* Acts 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 Design* Demonstrates 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 Enablement* Designs 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 Control* Establishes 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 Foundations* Applies solid 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 Excellence* Leads 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 Leadership* Brings 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 practices across AI engineering teams.
* Acts as a bridge between traditional application engineering and AI‐native development.8. AI Safety, Evaluation & Responsible AI Governance* Leads the implementation of AI evaluation and governance frameworks, including hallucination detection, confidence scoring, and human‐in‐the‐loop validation.
* Designs and enforces guardrails, moderation layers, and usage controls to prevent misuse or unintended outcomes.
* Partners with Risk, Compliance, Legal, and Security teams to embed Responsible AI principles into all GenAI solutions.
* Ensures GenAI adoption withstands audit, regulatory, and reputational scrutiny.9. Leadership, Influence & Execution* Operates as a hands‐on SVP, combining strategic influence wi

📌 Gen AI Engineering and Scaled AI Transformation (Winnipeg)
🏢 Citigroup
📍 Winnipeg