Gen AI Engineering and Scaled AI Transformation

Citi; Mississauga; On-site; CAD 120,000 - 160,000; Full time

Job summary

Citi is seeking an experienced professional specialized in Generative AI Engineering to lead transformative initiatives within the Source to Pay technology group. The incumbent will act as a senior technical authority on Large Language Models, guide deployment strategies, and design robust RAG architectures with a focus on reliability and governance.

The ideal candidate will demonstrate strong hands-on expertise, managing high-performance AI services, ensuring integration with broader ML ecosystems, and optimizing workflows for enterprise usage.

Qualifications

• 10+ years of experience in software engineering, ML, or AI platforms.
• 5+ years leading senior engineers and architects.
• 3+ years deploying LLM-based systems in production environments.
• Expertise in commercial and open-source LLM ecosystems.
• Proven ability to define enterprise-wide GenAI standards.

Responsibilities

• Act as a senior technical authority on Large Language Models.
• Lead the model selection and deployment strategy.
• Design and deliver robust RAG architectures.
• Establish prompt engineering standards.
• Lead deployment of GenAI systems into production.

Skills

Large Language Model Strategy

GenAI Application Development

RAG Architecture

Prompt Engineering

Machine Learning Foundations

Production Deployment

Software Engineering

AI Governance

Education

Bachelor's degree

Master's degree preferred

Tools

PyTorch

TensorFlow

Docker

FastAPI

Job description

Role Focus: Generative AI Engineering and Scaled AI Transformation for Source to Pay technology group - Hybrid

1. 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, private, or 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 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 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 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.

Recommended Qualifications

• 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.

Education

• Bachelor’s degree/University degree or equivalent experience
• Master’s degree preferred

Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law. If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity, review Accessibility at Citi. View Citi’s EEO Policy Statement and the Know Your Rights poster.