Staff Full Stack Development Engineer

Responsibilities:

• Operate as either a Staff Full Stack Development Engineer (Individual Contributor) or an Engagement Lead (Management Track).
• Own and deliver complex technical initiatives across multiple engagements or strategic programmes.
• Lead the design and implementation of enterprise-scale architectures and modernisation initiatives.
• Solve complex engineering challenges, including agentic AI architectures, evaluation frameworks, and COBOL-to-Java modernisation.
• Define and drive cross-program technical strategy, reference architectures, and engineering standards.
• Collaborate with enterprise architecture teams to align target-state solutions with business objectives.
• Design scalable, resilient, and high-performance enterprise integration solutions.
• Lead cloud transformation initiatives, including PCF-to-GCP migrations and IBM MQ-to-Kafka modernisation.
• Establish and drive AI-Driven Software Development Lifecycle (AIDLC) standards across engagements.
• Design and implement agentic AI architectures, small language model (SLM) strategies, and retrieval-augmented generation (RAG) solutions.
• Define AI evaluation frameworks and governance standards for enterprise AI applications.
• Present AI productivity metrics, ROI, and value realisation frameworks to client engineering leadership.
• Serve as a trusted technical advisor to senior client stakeholders and engineering leaders.
• Influence engineering practices and architectural decisions across multiple engagements.

Management Responsibilities (Engagement Lead):

• Lead multiple workstreams, PODs, or strategic engagements simultaneously.
• Manage and mentor a team of 3-6 engineers, fostering technical growth and career development.
• Own project planning, staffing, demand forecasting, and resource allocation.
• Drive delivery governance, business continuity planning (BCP), and program health.
• Monitor project KPIs, delivery metrics, risks, and overall client satisfaction.
• Lead estimation, delivery planning, and engineering pyramid optimisation.
• Coach engineering managers and technical leads while promoting team health and engagement.

Requirements:

• Required Technical Skills: Java and Spring Boot, microservices architecture, GitHub and GitHub Copilot, Maven, PostgreSQL, Docker, Jira and Confluence, Google Cloud Platform (GCP), Google Kubernetes Engine (GKE), Cloud Run, Cloud SQL, Pub/Sub, Apache Kafka, SonarQube, Harness CI/CD, Terraform, Camunda or Appian, Vertex AI, IBM Watsonx for Z (preferred), agentic AI frameworks (agents and skills), Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), enterprise integration patterns, and AI evaluation frameworks.

Leadership and Preferred Qualifications:

• Proven experience defining enterprise architecture and technical strategy across multiple programmes.
• Strong expertise in cloud-native application modernisation and legacy system transformation.
• Experience driving AI adoption and engineering productivity initiatives at scale.
• Excellent stakeholder management and executive communication skills.
• Demonstrated ability to mentor senior engineers and build high-performing teams.
• Strong delivery ownership with a focus on quality, scalability, and business outcomes.
• Recognised as a trusted advisor to client engineering leadership with impact beyond a single engagement.