About EPAS / Infrastructure Platforms
EPAS provides process and event-driven automation, user-driven run book automation, workflow orchestration, microservice APIs, and AI/ML capabilities as part of a wider Hyper Automation offering for Infrastructure Platforms. Key product areas include Automation as a Service (AaaS), Ansible Automation Platform (AAP), AutoM8, hybrid cloud automation, public cloud and mainframe enablement, SRE and observability capabilities, and AI-driven service management accelerators.
Job Responsibilities
Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems.
Produces architecture and design artifacts for complex applications while ensuring design constraints are met by software code development.
Lead the design and development of Java and Go-based services, APIs, automation tooling, and distributed platform capabilities across EPAS products such as AaaS, AAP, AutoM8, event-driven automation, and AI/ML-enabled service management.
Engineer capabilities that support key EPAS initiatives, including NextGen Automation migration to AAP, AutoM8 UI and AppStore enablement, hybrid public cloud automation, mainframe automation, RHEL lifecycle upgrades, Moneta boot upgrades, and AI-driven automation use cases such as AaaS MCP, VersionUp, IntelliPatch, and AaaS Utilities.
Drives decisions that influence the product design, application functionality, and technical operations and processes.
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Identifies and eliminates recurring operational issues by improving observability, automation, self-healing, service reliability, and recovery capability across EPAS platforms.
Partners with product, architecture, SRE, AI/ML, operational risk, and customer engineering teams to translate enterprise automation needs into secure, scalable, production-ready software solutions.
Adds to team culture of diversity, equity, inclusion, and respect.
Required Qualifications, Capabilities, and Skills
Formal training or certification on software engineering concepts and advanced applied experience.
Hands-on practical experience delivering system design, application development, testing, and operational stability.
Advanced experience in Java, Python and Go software engineering, including backend services, APIs, distributed systems, automation platforms, microservice frameworks, and operational tooling.
Proficiency in Java, Spring Boot, Go idioms, concurrency patterns, testing, and production service design.
Experience with automation and orchestration technologies, including Ansible, Ansible Automation Platform, event-driven automation, workflow orchestration, REST APIs, gRPC, messaging platforms, and resilient integration patterns.
Experience across the whole software development life cycle, including coding standards, code reviews, source control, build processes, testing, and operations.
Experience with CI/CD practices, automated quality gates, deployment automation, and release safety controls.
Experience with Linux, containers, Kubernetes, public cloud platforms, hybrid cloud architectures, infrastructure as code, service telemetry, logging, metrics, tracing, and observability practices.
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Practical understanding of secure engineering, technology lifecycle management, vulnerability remediation, controls readiness, auditability, and operational resilience in regulated enterprise environments.
Preferred Qualifications, Capabilities, and Skills
In-depth knowledge of the financial services industry and its technology controls, risk, and regulatory expectations.
Experience leading technical modernisation, service decomposition, platform engineering, migration, or lifecycle initiatives such as AAP adoption, application repave, RHEL lifecycle uplift, Java/JDK modernisation, Moneta upgrades, or migration to cloud-native services.
Caching technologies (e.g., Redis).
Infrastructure as Code (e.g., Terraform).
Resilient SQL and NoSQL databases and schema design (e.g., PostgreSQL, CockroachDB).
Kubernetes and public cloud knowledge (e.g., CKAD, AWS certifications).
Advanced knowledge of software applications and technical processes with considerable depth in one or more technical disciplines
