Production AI engineer

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.

• 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 GitOps 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 organisation processes.
• Operates within a highly regulated financial environment, maintaining 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.

• 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 (OpenAI, 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 LangChain, LangGraph, AutoGen, CrewAI, or equivalent — including multi-agent orchestration and tool-use patterns.
• Excellent engineering skills and strong understanding of Software Development Lifecycle, GitOps, and modern DevSecOps 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.
• Experience of delivering software using Agile delivery methodologies is a must (SCRUM/Kanban).
• 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.
• 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.
• Будет плюсом: 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, CloudFormation, Pulumi, etc.) is highly desirable; 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, AppDynamics, Dynatrace, etc.) is desirable; Degree in Computer Science, Mathematics, Physics, or a related technical subject is desirable

• business casual workplace with a hybrid working model (up to 2 days working at home per week)
• competitive base salary (which is annually reviewed)
• 27 days annual leave (plus bank holidays)
• A discretional 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