Key Responsibilities
1. Mainframe Modernisation & Platform Transformation
1. 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).
2. Conduct application assessments to identify migration candidates, define target-state architectures, and produce sequenced migration roadmaps with risk registers and rollback plans.
3. Establish reusable migration patterns, tooling, and runbooks to accelerate successive migration waves.
4. Leverage AI-assisted code translation tools (, autonomous AI coding agents such as Devin) to automate COBOL-to-Java conversion at scale, with human-in-the-loop review gates.
5. Validate functional parity post-migration through automated testing strategies (unit, integration, regression, performance).
2. Technology Operating Cost Reduction
6. Identify and quantify cost-reduction opportunities across MIPS consumption, software licensing, infrastructure footprint, and operational overhead.
7. Build and maintain a technology cost model; track savings realisation against committed targets on a monthly cadence.
8. Drive rationalisation of redundant systems, decommission end-of-life platforms, and consolidate tooling to reduce Total Cost of Ownership (TCO).
9. Partner with Finance and Vendor Management to renegotiate contracts and optimise spend through right-sizing, reserved capacity, and FinOps practices.
10. 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
11. Actively contribute to code reviews, architecture design sessions, and technical spike investigations as a practitioner — not just an observer.
12. Define and enforce engineering standards: coding conventions, API design (REST / gRPC), CI/CD pipeline standards, and security-by-design principles.
13. Lead adoption of modern Java ecosystem tooling: Spring Boot, Spring Cloud, Maven/Gradle, JUnit 5, Mockito, and Testcontainers.
14. Oversee containerisation and orchestration using Docker, Helm, and OpenShift / Kubernetes; govern CI/CD pipelines built on Tekton and Harness.
15. Ensure observability best practices: structured logging (Splunk), distributed tracing (OpenTelemetry ), and metrics dashboards (Prometheus / Grafana).
16. Govern secrets management via HashiCorp Vault and enterprise M2M authentication standards (OAuth / OIDC).
4. Generative AI & Agentic Development
17. 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 (, 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.
18. 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.
19. Evaluate and pilot AI coding assistants (Devin and equivalents) for autonomous code generation and refactoring; establish guardrails and human-in-the-loop review processes.
20. 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
21. Lead, mentor, and grow a team of 4-10 engineers across multiple squads, fostering a high-performance engineering culture.
22. Represent the India technology team in global forums, steering committees, and executive briefings; communicate programme status, risks, and decisions with clarity.
23. Build strong partnerships with global counterparts across the US, UK, and APAC to align on architecture decisions and delivery priorities.
24. Drive Agile / SAFe delivery practices: sprint planning, backlog grooming, retrospectives, and PI planning.
Required Qualifications
Education & Experience
25. Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related technical discipline.
26. 12–16 years of progressive software engineering experience, with at least 5 years in a senior technical leadership role (Tech Lead, Staff Engineer,principle).
27. Demonstrated experience leading or significantly contributing to at least one large-scale Mainframe modernisation or legacy platform migration programme.
28. 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 / 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
29. Practical familiarity with Large Language Models (LLMs) and their application in software engineering workflows.
30. Working knowledge of MCP (Model Context Protocol) for building agentic integrations between LLMs and enterprise systems.
31. Exposure to Google ADK (Agent Development Kit) or equivalent frameworks (LangChain, LangGraph, AutoGen) for multi-agent system development.
32. Awareness of AI coding assistants (Devin, GitHub Copilot, Cursor, or enterprise equivalents) and their role in accelerating engineering productivity.
33. Understanding of enterprise AI agent deployment platforms and the security, governance, and compliance considerations in a regulated environment.
34. Commitment to responsible AI: data privacy, model governance, hallucination mitigation, and human-in-the-loop design.
Nice to Have
35. Proficiency in Python for scripting, data pipelines, or AI/ML tooling integration.
36. Familiarity with Go (Golang) for high-performance microservices.
37. AWS, Azure, or GCP certifications (Solutions Architect, Developer Associate).
38. Certified Kubernetes Administrator (CKA) or equivalent.
39. SAFe Agilist or equivalent scaled Agile certification.
40. Experience with FinOps frameworks and cloud cost governance tooling.
41. Familiarity with Istio service mesh and circuit-breaking / rate-limiting patterns.
42. Experience with feature flags, canary deployments, and blue-green release strategies.
43. Experience with Database-as-a-Service platforms (Oracle, MongoDB, PostgreSQL, MS SQL) in an enterprise managed environment.
44. Prior experience working in a global matrix organisation with distributed teams across multiple time zones.
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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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