Production Engineer - 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 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 implementsagentic AI workflows— building autonomous systems that can reason, plan, and act across production environments with minimal human intervention.

• Applies advancedprompt engineering techniquesto integrate large language models (LLMs) into operational tooling, incident response pipelines, and developer productivity platforms.

• Leads the development ofAI-native observabilitysolutions — 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.

• DrivesContinuous Delivery and Automationefforts 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 theAgile Frameworkthrough 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 criticalsoftware engineering or production engineering rolewith high business impact and a strong programming foundation (Java, Python, Go, or equivalent).

• Hands-on experience withAI/ML engineering— including working with LLM APIs (OpenAI, Anthropic, Gemini, or open-source equivalents), embedding models, and vector databases.

• Proven expertise inprompt engineering: designing, iterating, and evaluating prompts for production use cases including classification, summarisation, code generation, and autonomous decision-making.

• Experience designing and deployingagentic systemsusing frameworks such asLangChain,LangGraph,AutoGen,CrewAI, or equivalent — including multi-agent orchestration and tool-use patterns.

• Excellent engineering skills and strong understanding ofSoftware Development Lifecycle, GitOps, and modernDevSecOpspractices.

• 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 ofDocker/Kubernetesstack and a service mesh (Istio or equivalent) is highly desirable.

• Operational experience with orchestration tools forCI/CD and Infrastructure-as-Codetooling (Terraform, CloudFormation,Pulumi, etc.) is highly desirable.

• Experience of delivering software usingAgile delivery methodologiesis a must (SCRUM/Kanban).

• Operational experience of usingmiddleware technologies(MQ, Apache Kafka, etc.) to run services at scale is desirable.

• Strong experience withend-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.

Human Qualities & Soft Skills:

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 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.