Production AI Engineer - Vice President - Citi

Discover your future at Citi Working at Citi is far more than just a job. A career with us means joining a team of approximately 219,000 dedicated people from around the globe. At Citi, you'll have the opportunity to grow your career, give back to your community and make a real impact. Job Overview 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 agentsa, generative AI, and self-ahealing 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 India 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. Applies in-depth understanding of the business impact of technical contributions. Accountable for delivery of a full range of end-to-end projects. Excellent communication skills required to negotiate internally. Involved in short- to medium-term planning of actions and resources for own 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 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. 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 (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. • 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. • 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, AppDynamics, 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. Beyond technical capability, the Production Engineer who will thrive in this role brings a distinct set of human qualities that amplify their engineering impact and elevate those around them. • 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 ambigui