Senior Production Engineer - Senior Vice President

The Production Engineer is a pivotal role within Citi's Technology organisation, responsible for designing, building, and operating the intelligent systems that underpin our global production environment.

This is an engineering‑first position at the intersection of software craftsmanship, AI‑native development, and large‑scale distributed systems.

As part of a multi‑year transformation journey, the successful candidate will help define what production engineering looks like in an era of autonomous agents, generative AI, and self‑healing infrastructure.

You will be expected to write production‑grade code daily, design agentic workflows, and contribute meaningfully to the evolution of our AI engineering practices across Citi's technology hub.

The role requires a comprehensive understanding of multiple areas within a function and how they interact to achieve the objectives of the function.

It applies an in‑depth understanding of the business impact of technical contributions and is accountable for delivery of a full range of end‑to‑end projects.

Excellent communication skills are required to negotiate internally.

The role is involved in short‑ to medium‑term planning of actions and resources for its area.

Responsibilities

• Designs, develops, and maintains production‑grade software systems with a strong emphasis on reliability, scalability, and operational excellence across Citi's global technology estate.
• Architects and implements agentic AI workflows — building autonomous systems that can reason, plan, and act across production environments with minimal human intervention.
• Applies advanced prompt engineering techniques to integrate large language models (LLMs) into operational tooling, incident response pipelines, and developer productivity platforms.
• Leads the development of AI‑native observability solutions — leveraging intelligent agents to detect anomalies, predict failures, and automate remediation before issues impact end users.
• Writes clean, well‑tested, and well‑documented code across the full stack; champions engineering best practices including code review, pair programming, and test‑driven development.
• Drives Continuous Delivery and Automation efforts across supported applications by means of Root Cause Analysis reviews, knowledge management, performance tuning, and user training.
• Operates and evolves CI/CD pipelines, Infrastructure‑as‑Code tooling, and Git Ops workflows to support rapid, safe delivery of software at scale.
• Collaborates with platform, data, and product engineering teams to embed AI capabilities into the production lifecycle — from deployment to decommission.
• Implements the Agile Framework through one of its implementations (SCRUM or Kanban) and ensures it integrates with overall organisational processes.
• Operates within a highly regulated financial environment, maintaining an in‑depth understanding of compliance requirements and their implications for system design and data handling.
• Coaches and mentors team members on AI engineering practices, prompt design patterns, and agentic system architecture — fostering a culture of continuous learning and technical excellence.
• Avidly communicates progress and project status across the organisation and ensures that stakeholders are managed appropriately throughout the execution period.
• Fosters a culture that promotes transparency and innovation for increased team productivity.

Qualifications

• Demonstrable experience in a critical software engineering or production engineering role with high business impact and a strong programming foundation (Java, Python, Go, or equivalent).
• Hands‑on experience with AI/ML engineering — including working with LLM APIs (Open AI, Anthropic, Gemini, or open‑source equivalents), embedding models, and vector databases.
• Proven expertise in prompt engineering: designing, iterating, and evaluating prompts for production use cases including classification, summarisation, code generation, and autonomous decision‑making.
• Experience designing and deploying agentic systems using frameworks such as Lang Chain, Lang Graph, Auto Gen, Crew AI, or equivalent — including multi‑agent orchestration and tool‑use patterns.
• Excellent engineering skills and strong understanding of Software Development Lifecycle, Git Ops, and modern Dev Sec Ops practices.
• Excellent working knowledge of key computer science concepts (networking, operating systems, virtualisation, containerisation, etc.).
• Polyglot full‑stack developer mentality and ability to pick up new languages and skills.
• Excellent debugging and analytical skills: ability to isolate root cause across networking/infrastructure, application, and database stacks.
• Operational experience of deploying and running services at scale on top of Docker/Kubernetes stack and a service mesh (Istio or equivalent) is highly desirable.
• Operational experience with orchestration tools for CI/CD and Infrastructure‑as‑Code tooling (Terraform, Cloud Formation, Pulumi, etc.) is highly desirable.
• Experience of delivering software using Agile delivery methodologies is a must (SCRUM/Kanban).
• Operational experience of using middleware technologies (MQ, Apache Kafka, etc.) to run services at scale is desirable.
• Strong experience with end‑to‑end observability stacks (Datadog, App Dynamics, Dynatrace, etc.) is desirable.
• Degree in Computer Science, Mathematics, Physics, or a related technical subject is desirable.
• Experience of senior stakeholder management.
• Consistently demonstrates clear and concise written and verbal communication skills.
• Ability to operate in a global environment with on‑/near‑/off‑shore matrix reporting structures.
• Human Qualities & Soft Skills
• Learnability — Rapidly acquires new skills, frameworks, and paradigms. In a field evolving as fast as AI engineering, the ability to learn is the most durable skill of all.
• Teachability — Receives feedback with openness and intellectual humility. Actively seeks mentorship and applies guidance to accelerate growth.
• Flexibility & Adaptability — Thrives in ambiguity. Pivots gracefully when requirements shift, technology evolves, or priorities change — without losing momentum or quality.
• Engineering Mindset — Approaches every problem systematically: decomposing complexity, forming hypotheses, and validating solutions with rigour and precision.
• Product‑Minded Thinking — Understands that code serves users and business outcomes. Balances technical elegance with pragmatic delivery and user impact.
• Collaborative Spirit — Builds trust across disciplines — engineering, product, operations, and leadership.

Elevates the team’s collective output through generosity and clear communication.

• Intellectual Curiosity — Asks “why” before “how”. Explores the edges of what’s possible with AI and production systems, driven by genuine fascination rather than obligation.
• Ownership & Accountability — Takes end‑to‑end responsibility for what they build. Does not hand off problems — follows through from design to deployment to post‑incident review.
• What we’ll provide you
• 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 Employer

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.

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