Working at Citi is far more than just a job. A career with us means joining a team of more than 230,000 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.
Key Responsibilities
1. Mainframe Modernisation & Platform Transformation
- Own and drive end-to-end migration of legacy Mainframe workloads (COBOL, JCL, CICS, IMS, DB2/z) to modern Java-based microservices deployed on enterprise container platforms (OpenShift / Kubernetes).
- Conduct application assessments to identify migration candidates, define target-state architectures, and produce sequenced migration roadmaps with risk registers and rollback plans.
- Establish reusable migration patterns, tooling, and runbooks to accelerate successive migration waves.
- Leverage AI-assisted code translation tools (e.g., autonomous AI coding agents such as Devin) to automate COBOL-to-Java conversion at scale, with human-in-the-loop review gates.
- Validate functional parity post-migration through automated testing strategies (unit, integration, regression, performance).
2. Technology Operating Cost Reduction
- Identify and quantify cost-reduction opportunities across MIPS consumption, software licensing, infrastructure footprint, and operational overhead.
- Build and maintain a technology cost model; track savings realisation against committed targets on a monthly cadence.
- Drive rationalisation of redundant systems, decommission end-of-life platforms, and consolidate tooling to reduce Total Cost of Ownership (TCO).
- Partner with Finance and Vendor Management to renegotiate contracts and optimise spend through right-sizing, reserved capacity, and FinOps practices.
- Introduce engineering efficiency metrics (deployment frequency, lead time, MTTR) to demonstrate productivity gains that translate to measurable cost avoidance.
3. Hands-On Engineering & Technical Leadership
- Actively contribute to code reviews, architecture design sessions, and technical spike investigations as a practitioner — not just an observer.
- Define and enforce engineering standards: coding conventions, API design (REST / gRPC), CI/CD pipeline standards, and security-by-design principles.
- Lead adoption of modern Java ecosystem tooling: Spring Boot, Spring Cloud, Maven/Gradle, JUnit 5, Mockito, and Testcontainers.
- Oversee containerisation and orchestration using Docker, Helm, and OpenShift / Kubernetes; govern CI/CD pipelines built on Tekton and Harness.
- Ensure observability best practices: structured logging (Splunk), distributed tracing (OpenTelemetry ), and metrics dashboards (Prometheus / Grafana).
- Govern secrets management via HashiCorp Vault and enterprise M2M authentication standards (OAuth 2.0 / OIDC).
4. Generative AI & Agentic Development
- Champion the adoption of enterprise AI tooling across engineering teams, including:
- Secure LLM API Gateways (equivalent to Azure OpenAI/litellm / Vertex AI enterprise proxies) for governed, audited access to large language models.
- AI-powered developer workspaces for productivity acceleration (e.g., GitHub Copilot, Devin, Claude, or equivalent enterprise AI coding assistants).
- AI-driven automated code review frameworks — embedding GenAI-powered PR and end-to-end review into CI/CD workflows.
- Enterprise AI Agent Platforms (OpenShift-based) for secure, scalable deployment and lifecycle management of AI agents in a regulated environment.
- Drive agentic development using Google ADK (Agent Development Kit) and MCP (Model Context Protocol) to build multi-agent workflows that automate test generation, documentation, incident triage, and code migration tasks.
- Evaluate and pilot AI coding assistants (Devin and equivalents) for autonomous code generation and refactoring; establish guardrails and human-in-the-loop review processes.
- Ensure all AI tooling usage complies with data classification policies, enterprise authentication standards, and AI governance frameworks applicable in a regulated financial services environment.
5. People Leadership & Stakeholder Management
- Lead, mentor, and grow a team of 4-10 engineers across multiple squads, fostering a high-performance engineering culture.
- Represent the India technology team in global forums, steering committees, and executive briefings; communicate programme status, risks, and decisions with clarity.
- Build strong partnerships with global counterparts across the US, UK, and APAC to align on architecture decisions and delivery priorities.
- Drive Agile / SAFe delivery practices: sprint planning, backlog grooming, retrospectives, and PI planning.
Required Qualifications
Education & Experience
- Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related technical discipline.
- 13–18+ years of progressive software engineering experience, with at least 5-8 years in a senior technical leadership role (Tech Lead, Staff Engineer, principle).
- Demonstrated experience leading or significantly contributing to at least one large-scale Mainframe modernization or legacy platform migration programme.
- Prior experience in financial services, banking, or a similarly regulated industry strongly preferred.
Technical Stack
Languages
Java 17/21 (Expert), Python, COBOL / JCL (reading & assessment level)
Frameworks
Spring Boot, Spring Cloud, Spring Security, Spring Data JPA, Project Reactor / WebFlux
Platform & Containers
OpenShift, Kubernetes, Docker, Helm
CI/CD
Tekton, Harness, Jenkins, Git (Bitbucket / GitHub), Artifactory, SonarQube
Databases
Oracle, MongoDB, PostgreSQL, MS SQL Server, Redis, DB2/z
Messaging
Apache Kafka, IBM MQ
API
REST, gRPC, OpenAPI / Swagger, Service Mesh (Istio)
Observability
Prometheus, Grafana, Splunk, OpenTelemetry, Jaeger
Security & Auth
HashiCorp Vault, OAuth 2.0 / OIDC, SAST / DAST tooling
AI & Agentic
LLM API Gateways (Azure OpenAI / Vertex AI), MCP, Google ADK, AI coding assistants (Devin / Copilot), AI agent platforms
AI & Agentic Ecosystem
- Practical familiarity with Large Language Models (LLMs) and their application in software engineering workflows.
- Working knowledge of MCP (Model Context Protocol) for building agentic integrations between LLMs and enterprise systems.
- Exposure to Google ADK (Agent Development Kit) or equivalent frameworks (LangChain, LangGraph, AutoGen) for multi-agent system development.
- Awareness of AI coding assistants (Devin, GitHub Copilot, Cursor, or enterprise equivalents) and their role in accelerating engineering productivity.
- Understanding of enterprise AI agent deployment platforms and the security, governance, and compliance considerations in a regulated environment.
- Commitment to responsible AI: data privacy, model governance, hallucination mitigation, and human-in-the-loop design.
Nice to Have
- Proficiency in Python for scripting, data pipelines, or AI/ML tooling integration.
- Familiarity with Go (Golang) for high-performance microservices.
- AWS, Azure, or GCP certifications (Solutions Architect, Developer Associate).
- Certified Kubernetes Administrator (CKA) or equivalent.
- SAFe Agilist or equivalent scaled Agile certification.
- Experience with FinOps frameworks and cloud cost governance tooling.
- Familiarity with Istio service mesh and circuit-breaking / rate-limiting patterns.
- Experience with feature flags, canary deployments, and blue-green release strategies.
- Experience with Database-as-a-Service platforms (Oracle, MongoDB, PostgreSQL, MS SQL) in an enterprise managed environment.
- Prior experience working in a global matrix organisation with distributed teams across multiple time zones.
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Technology
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Applications Development
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Full time
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Please see the requirements listed above.
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For complementary skills, please see above and/or contact the recruiter.
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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.
