Overview

We're looking for a talented AI Engineer with proven industry experience in building and shipping production-grade AI/ML at scale to help build our next-generation Developer Productivity platform at Citi using bleeding-edge Generative AI.

You'll be joining a small, high-impact teams challenging, changing, modernising and enhancing the experience of our 50,000 engineers globally throughout Citi's SDLC (Software Development Life Cycle).

Where you'll work

You'll sit within Development Enablement, part of Citi's CTO organisation, which is a group focused on innovation, developer platforms and internal tools used across the bank, where we experiment and ship fast, like a startup inside a global bank.

Why you'll love working here

We deliver disruptive solutions at scale: our team has been founded by true intrapreneurs who have brought positive impact to the entire bank, and we value creativity and initiative.
We are small and dangerous: we design our team to be formed by small high-skilled squads building, shipping to production and celebrating success together.
Ship greenfield AI to 50,000 developers: as part of the CTO organisation, you will design and build software that will positively impact lives and productivity of thousands of developers globally across Citi.

By day 45 - learning how to ship AI software at Citi

Being one of the largest financial institutions in the world, the initial part of your journey will be about learning how to efficiently ship software at Citi and understand the web of stakeholders who will enable you to deliver at your best, including the ones involved in the approvals required to ship.

By the end of this stage, you will have:

Started a relationship with the engineering leads within Developer Enablement.
Started learning our experiment-driven AI development workflow.
Chosen and deployed an Experiment Tracking and a Data Labelling tool.
Consolidated existing evaluation scripts into an existing Evaluation CLI and release it.
Devised and executed on at least one Quantitative Test Plan for a GenAI solution.
Helped in providing expert clarification regarding our AI methodology and solution to stakeholders outside CTO.

By 3 months - become a citizen within CTO's AI and represent as an AI expert

As an SVP engineer, you will be expected to foster cross-team collaboration and help overcome inefficiencies. Within three months, you will already have a consolidated understanding of the roles of engineering leads within Developer Experience, and you will have started working with engineering leads across CTO.

By the end of this stage, you will have:

Regular interactions with Engineering Leads across Developer Enablement.
Started a relationship with engineering leads in the AI space across CTO.
Mastered and applied routinely our experiment-driven AI development workflow.
Completed a few AI experiments that delivered business features.
Identified and disrupted at least one inefficiency in our AI Development Workflow.
Expanded our suite of eval metrics and techniques to simplify agentic-AI development.
Represented Developer Enablement with regard to our AI methodology and solution to stakeholders outside on at least one occasion.

By 6 months - presence in CTO's AI and own experiment-driven AI development workflow

Continuing your journey as an SVP engineer, you will have identified and started engaging on at least one opportunity of cross-team collaboration that addresses a developer productivity inefficiency inside CTO or another organisation that we advise. These relationships will have started providing you with enough resourcefulness to start designing AI solutions from scratch aligned with strategic tools.

By the end of this stage, you will have:

Regular interactions with Engineering Leads across CTO to unblock shipping to production.
Started owning and evolving our experiment-driven AI development workflow.
Started owning and evolving our AI evaluation Tooling.
Routinely completed AI experiments that delivered business features.
Routinely shipped software features to production, end-to-end.
Contributed to designing new production-grade AI applications or modules.
Further expanded our suite of eval metrics and techniques to simplify agentic-AI development.
Represented Developer Enablement with regard to our AI methodology and solution to stakeholders outside on multiple occasions.

By 12 months - own our team's AI experimentation machine

At this point, you will have an opinionated view on how to take our experiment-driven AI development workflow to the next level, training other younger (Grads, AVP and VPs) to perform confidently different parts of this workflow.

By the end of this stage, you will have:

Regular interactions with Engineering Leads across CTO to unblock shipping to production.
Advocacy and dissemination of our experiment-driven AI development workflow.
Routinely completed critical path AI experiments that delivered business features.
Designed and implemented production-grade features for AI solutions.
Proven to mitigate AI production deployment risk for our AI Engineering Team.
Helped scoping and planning future AI development work.
Further expanded our suite of eval metrics and techniques to simplify agentic-AI development.
Represented Developer Enablement with regard to our AI methodology and solution to stakeholders outside on multiple occasions.

What you will bring

Proficiency in AI Evaluation: regression, classification, information retrieval, power analysis and correlation and statistical testing, and others
Strong understanding of AI/ML fundamentals: Deep Learning, LLMs and implications
Strong server-side Engineering: Python, REST APIs, asynchronous and functional programming
Proficiency with relational and/or NoSQL databases: PostgreSQL, MongoDB.
Experience with message queuing systems: Apache Kafka.
Deep understanding of containerisation (Docker) and orchestration (Kubernetes).
Familiarity with CI/CD tools (e.g., Jenkins, Tekton, ArgoCD, Harness)
Comfortable coaching software engineers (with no AI experience) in working with an experiment-driven development workflow

Tools and technologies you might use

Python, FastAPI, Pydantic, Pandas, Scikit-learn, NLTK, PostgreSQL, MongoDB, Apache Kafka, Docker, Kubernetes, Helm, Tekton, Harness
Mac or PC - it's up to you
Access to time-saving AI tools such as GitHub Copilot

What we'll provide you

By joining Citi Belfast, you will not only be part of a business casual workplace with a hybrid working model (up to 2 days working at home per week), but also receive a competitive base salary (which is annually reviewed), and enjoy a whole host of additional benefits such as:

27 days annual leave (plus bank holidays)
A discretionary annual performance related bonus
Private Medical Care & Life Insurance
Employee Assistance Program
Pension Plan
Paid Parental Leave
Special discounts for employees, family, and friends
Access to an array of learning and development resources

Equal opportunity statement and accessibility information are provided as part of Citi's recruitment policy.

Citigroup Inc.

Latest Jobs

Staff AI Engineer - CTO Developer Engineering (SVP)