Gen AI Engineering and Scaled AI Transformation

Citi•Mississauga, ON

8d•CA$145,100 - CA$217,700•Hybrid

About The Position

This role focuses on Generative AI Engineering and Scaled AI Transformation within the Source to Pay technology group, operating in a hybrid work environment. The position requires a senior technical authority on Large Language Models (LLMs), leading strategy, selection, and deployment. It involves hands-on design and development of GenAI applications and agentic systems, with a strong emphasis on Retrieval Augmented Generation (RAG) for enterprise knowledge enablement. The role also encompasses prompt engineering, workflow optimization, cost control, and ensuring production readiness, scalability, and operational excellence. A solid foundation in Machine Learning/Deep Learning and software engineering leadership is crucial, along with a commitment to AI safety, evaluation, and responsible AI governance. The individual will operate as a hands-on leader, influencing strategy and driving technical execution, while effectively communicating complex concepts to diverse stakeholders and delivering measurable business value in an agile environment.

Requirements

10+ years of progressive experience in software engineering, ML, or AI platforms, with 5+ years leading senior engineers and architects.
3+ years of hands‑on experience deploying LLM‑based systems in production environments at enterprise scale.
Demonstrated authority across commercial and open‑source LLM ecosystems (e.g., OpenAI, Anthropic, Google, Llama), including model selection, fine‑tuning, and hosting strategies.
Proven ability to define enterprise-wide GenAI standards, reference architectures, and reusable accelerators.
Demonstrated leadership in establishing prompt engineering standards and orchestration patterns.
Experience optimizing latency, throughput, accuracy, and token cost across large‑scale GenAI workloads.
Bachelor’s degree/University degree or equivalent experience
Master’s degree preferred

Nice To Haves

Hands-on GenAI application & Agentic System Design using LangChain, LangGraph, LlamaIndex, and Hugging Face.
Experience with vector databases and embedding strategies (pgvector, Pinecone, Weaviate, FAISS).
Experience with PyTorch and TensorFlow for embeddings, training pipelines, and fine-tuning.
Experience deploying GenAI systems using Docker, cloud-native architectures, and hardened APIs.
Experience with AI safety, evaluation, and responsible AI governance frameworks.
Experience in software engineering leadership, setting standards for Python-based GenAI services.
Experience developing high-performance AI-powered APIs using FastAPI and async programming patterns.

Responsibilities

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.
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.
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.
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.
Applies 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.
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.
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.
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.
Operates as a hands‑on SVP, combining strategic influence with deep technical execution.
Leads senior engineers and GenAI specialists, building sustainable internal AI capability rather than point solutions.
Communicates complex GenAI concepts clearly to executive and non‑technical stakeholders.
Drives delivery in agile, fast‑moving environments, with a strong bias for outcomes and measurable value.