Gen AI Engineering and Scaled AI Transformation Job at Citi (Mississauga)

Job Responsibility

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

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

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Gen AI Engineering and Scaled AI Transformation

Job Responsibility

Requirements

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