Java Engineering Manager, Cloud Native & AI Development -Senior Vice President

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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