Gen AI Engineering and Scaled AI Transformation

Citi; New York (NY); Hybrid; USD 127,967 - 184,841; Full time

Job summary

Citi is seeking a senior GenAI Engineering leader for its Mississauga, Canada location. The role blends strategic authority on LLMs with hands‑on design and deployment of enterprise GenAI solutions in a hybrid environment.

You will guide model selection, tooling, and governance, while delivering scalable, reliable AI services across Source to Pay technology. This leadership position requires deep ML/DL grounding and strong collaboration with Risk, Compliance, and Security teams.

Qualifications

• 10+ years in software engineering, ML, or AI platforms.
• 5+ years leading senior engineers and architects.
• Proven authority across commercial and open‑source LLM ecosystems.
• Ability to define enterprise GenAI standards and reference architectures.
• Experience deploying GenAI at enterprise scale.

Responsibilities

• Serve as senior technical authority on LLM strategy and lifecycle management.
• Lead hands‑on GenAI apps using LangChain, LangGraph, LlamaIndex, Hugging Face.
• Design robust Retrieval Augmented Generation (RAG) architectures.
• Establish prompt engineering standards and orchestration patterns.
• Ensure scalable, observable, and secure AI production systems.

Skills

Experience leading senior engineers

GenAI/ML platforms

LLM ecosystems (OpenAI, Anthropic, etc

Reference architectures & accelerators

Prompt engineering & orchestration

Education

Bachelor’s degree or equivalent

Master’s degree preferred

Job description

Gen AI Engineering and Scaled AI Transformation

Location: Mississauga, Ontario, Canada | Job Type: Hybrid

Job Req Id: 26979423

Posted: Jul. 17, 2026

Overview

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

Responsibilities

1. Large Language Model (LLM) Strategy & Technical Authority
   • Act as a senior technical authority on Large Language Models, including commercial and open‑source ecosystems (OpenAI, Gemini, Claude, Llama).
   • Lead model selection and deployment strategy, balancing use‑case fit, data sensitivity, cost efficiency, latency, accuracy, and regulatory constraints.
   • Guide decisions on hosted vs. private vs. fine‑tuned models, ensuring optimal trade‑offs between performance, control, and operational risk.
   • Establish enterprise standards for LLM lifecycle management, including upgrades, regression validation, and decommissioning.
2. Hands‑On GenAI Application & Agentic System Design
   • Demonstrate hands‑on leadership in building GenAI applications using LangChain, LangGraph, LlamaIndex, and Hugging Face, translating experimentation into production systems.
   • Architect agentic and multi‑step workflows, enabling tool‑use, reasoning chains, state management, and orchestration at enterprise scale.
   • Set reusable reference patterns and accelerators for GenAI adoption across application teams.
   • Ensure solutions are built with enterprise‑grade reliability, explainability, and extensibility.
3. Retrieval Augmented Generation (RAG) & Enterprise Knowledge Enablement
   • Design and deliver robust RAG architectures that ground GenAI outputs in trusted, auditable enterprise data.
   • Lead implementation of vector databases and embedding strategies (pgvector, Pinecone, Weaviate, FAISS), aligned with data access and security models.
   • Apply advanced retrieval techniques including hybrid search, re‑ranking, metadata filtering, and context optimization to improve response accuracy and relevance.
   • Ensure RAG solutions support data lineage, auditability, and regulatory compliance.
4. Prompt Engineering, Workflow Optimization & Cost Control
   • Establish prompt engineering and orchestration standards to ensure consistency, maintainability, and quality across GenAI solutions.
   • Optimize GenAI workflows by actively managing latency, throughput, token cost, and accuracy trade‑offs in production environments.
   • Implement evaluation and experimentation frameworks to continuously improve output quality and business value.
   • Drive disciplined use of caching, batching, fallback models, and token optimization techniques.
5. Machine Learning & Model Enablement Foundations
   • Apply strong grounding in ML/DL fundamentals, enabling informed architectural decisions and credible engagement with data science teams.
   • Leverage PyTorch and TensorFlow for embeddings, training pipelines, and targeted fine‑tuning where business value is clear.
   • Ensure GenAI capabilities integrate seamlessly into the broader ML, data, and MLOps ecosystem.
   • Balance rapid GenAI delivery with long‑term model sustainability and governance.
6. Production Deployment, Scalability & Operational Excellence
   • Lead deployment of GenAI systems into secure, scalable production environments using Docker, cloud‑native architectures, and hardened APIs.
   • Establish observability and monitoring for GenAI applications, covering performance, drift, quality, reliability, and failure modes.
   • Ensure GenAI platforms meet enterprise availability, resilience, and disaster recovery expectations.
   • Drive operational readiness, incident management, and ongoing optimization of AI services.
7. Software Engineering Leadership
   • Bring strong hands‑on software engineering credibility, setting standards for Python‑based GenAI services.
   • Lead development of high‑performance AI‑powered APIs using FastAPI and async programming patterns.
   • Champion clean architecture, testability, and security best practices across AI engineering teams.
   • Act as a bridge between traditional application engineering and AI‑native development.
8. AI Safety, Evaluation & Responsible AI Governance
   • Lead the implementation of AI evaluation and governance frameworks, including hallucination detection, confidence scoring, and human‑in‑the‑loop validation.
   • Design and enforce guardrails, moderation layers, and usage controls to prevent misuse or unintended outcomes.
   • Partner with Risk, Compliance, Legal, and Security teams to embed Responsible AI principles into all GenAI solutions.
   • Ensure GenAI adoption withstands audit, regulatory, and reputational scrutiny.
9. Leadership, Influence & Execution
   • Operate as a hands‑on SVP, combining strategic influence with deep technical execution.
   • Lead senior engineers and GenAI specialists, building sustainable internal AI capability rather than point solutions.
   • Communicate complex GenAI concepts clearly to executive and non‑technical stakeholders.
   • Drive 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

Equity, Inclusion & Accessibility

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