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 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.
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.
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Job Family Group:
Technology
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Job Family:
Applications Development
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Time Type:
Full time
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Most Relevant Skills
Please see the requirements listed above.
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Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
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