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 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 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

Job Family Group: Technology

Job Family: Applications Development

Time Type: Full time

Primary Location Full Time Salary Range: $145,100.00 - $217,700.00

Most Relevant Skills Please see the requirements listed above.

Other Relevant Skills For complementary skills, please see above and/or contact the recruiter.

Automated Processing and AI We use automated processing, including artificial intelligence, for our legitimate business interests (or our reasonable and appropriate business purposes) to identify and align the candidate's skills and abilities with a specific job opening. Additionally, if you so choose, or consent, we can match your skills and abilities to other suitable roles at Citi.

Importantly, all our hiring processes and decisions, including determining your suitability for a role, are conducted, checked, and decided by individuals. Our automated processing and AI do not involve relying on automatic or autonomous decision-making. Please refer to any Jurisdictional Considerations, with specific provisions for your country (where relevant) for further details.

This job opening is for an existing job vacancy.

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 (https://www.citigroup.com/citi/accessibility/application-accessibility.htm).

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Minority/Female/Veteran/Individuals with Disabilities/Sexual Orientation/Gender Identity.

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