Job Description
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). 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. 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). 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. 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.
