Staff Software Engineer

Role: Staff Software Engineer - Forward Deployment Engineer (FDE)

Bengaluru/Chennai - Hybrid (3 days in Office per week)

Pearson is the world’s leading learning company, dedicated to helping people make progress in their lives through education.

We deliver high-quality content, assessments, and digital services to learners, educators, and institutions globally.

Our mission is to empower every learner to achieve their potential.

We are seeking a Staff Software Engineer - Forward Deployment Engineer (FDE) who thrives in ambiguity and is passionate about transforming complex business challenges into measurable outcomes.

The Forward Deployment Engineer operates at the intersection of business strategy, product thinking, software engineering, cloud engineering, security, and AI-assisted delivery.

This role partners directly with business leaders, product teams, architects, engineers, and technology partners to discover problems, shape solutions, build production-ready capabilities, establish reusable engineering patterns, and enable sustainable ownership across the organisation.

The successful candidate combines strategic thinking with hands-on execution and can rapidly move from business need to production outcome.

Success in this role requires the ability to:

• Solve the right problem.
• Deliver measurable outcomes.
• Establish repeatable engineering patterns.
• Accelerate engineering delivery.
• Enable permanent teams.
• Leave the organisation stronger after every engagement.

• Partner with business, product, and technology stakeholders to identify and solve high-value business problems.
• Analyse customer journeys, business processes, operating models, and technical ecosystems.
• Translate ambiguous needs into clearly defined capabilities and measurable outcomes.
• Challenge assumptions and identify underlying root causes.
• Facilitate discovery workshops, design sessions, and rapid validation activities.
• Develop executable solution strategies aligned with business objectives.

• Design and deliver production-ready business capabilities.
• Drive initiatives from concept through implementation, adoption, and operational readiness.
• Make pragmatic trade-offs across speed, quality, scalability, security, maintainability, and cost.
• Build critical solution components and reference implementations when required.
• Remove delivery blockers through cross-functional collaboration.
• Ensure delivered capabilities generate measurable business value.

• Design solutions across enterprise applications, APIs, cloud services, data platforms, AI services, and legacy technologies.
• Apply architectural patterns including event-driven architecture, microservices architecture, domain-driven design, workflow orchestration, and enterprise integration patterns where appropriate.
• Simplify complex business and technology ecosystems.
• Establish scalable, resilient, and maintainable solution foundations.
• Define clear ownership boundaries, interfaces, standards, and engineering practices.

• Design, build, and operate cloud-native solutions across AWS and/or Azure environments.
• Apply Infrastructure as Code, Terraform, Kubernetes, automation, deployment pipelines, observability, and operational excellence practices.
• Design solutions that are scalable, resilient, secure, and operationally sustainable.
• Optimise performance, reliability, availability, and cost efficiency of cloud-based workloads.
• Implement monitoring, logging, tracing, and operational controls to ensure production readiness.
• Contribute to reusable cloud engineering patterns, standards, and engineering accelerators.
• Partner with engineering, security, and operations teams to establish cloud-native best practices.

• Embed security, privacy, compliance, resilience, and governance into solution discovery, design, and implementation activities.
• Apply Secure-by-Design and Security-by-Default principles throughout the software lifecycle.
• Conduct threat modelling, risk analysis, and security architecture reviews.
• Design secure authentication, authorisation, encryption, secrets management, and data protection controls.
• Partner with Security and Risk teams to ensure compliance with enterprise standards.
• Promote DevSecOps practices including automated security testing, vulnerability management, dependency validation, policy enforcement, and continuous compliance.

• Leverage AI-assisted engineering to accelerate discovery, development, testing, troubleshooting, documentation, and operational support.
• Apply agentic workflows, automation frameworks, tool orchestration, and human-in-the-loop approaches where they provide measurable value.
• Establish responsible AI guardrails covering governance, accountability, security, and data protection.
• Drive measurable improvements in engineering productivity and delivery velocity.
• Evaluate emerging AI capabilities and their practical application within enterprise environments.

• Establish reusable engineering patterns, accelerators, frameworks, and implementation blueprints.
• Improve software quality through automation, testing, observability, operational readiness, and engineering standards.
• Create enterprise assets that can be adopted beyond a single initiative.
• Drive standardisation through implementation and enablement.
• Promote sustainable and repeatable engineering practices.

• Work directly with delivery teams throughout implementation.
• Mentor engineers and raise technical capability across teams.
• Establish ownership models, documentation, support processes, operational runbooks, and automation.
• Enable permanent teams to independently operate and evolve delivered capabilities.
• Measure success through sustainable team ownership rather than ongoing dependency.

• Communicate technical decisions in terms of business value, customer impact, risk, speed, and investment efficiency.
• Present solution options, trade-offs, and recommendations to senior leaders.
• Influence stakeholders without relying on organisational authority.
• Help leaders make informed technology investment decisions.
• Provide clarity on risks, constraints, priorities, and delivery strategies.

• Proven ability to operate effectively in highly ambiguous environments.
• Strong understanding of business processes, customer journeys, and value creation.
• Ability to move from need → problem → capability → solution → implementation.
• Excellent judgement regarding simplification, prioritisation, and scope management.
• Demonstrated ownership of measurable business outcomes.

• Strong software engineering experience in Python, TypeScript, Java, or equivalent technologies.
• Experience designing and developing distributed systems and production-grade applications.
• Deep understanding of enterprise architecture, microservices architecture, distributed systems, and modern software design principles.
• Experience building scalable, resilient, and maintainable systems.

• Strong experience designing and delivering solutions on AWS and/or Azure.
• Experience with Infrastructure as Code, Terraform, Kubernetes, containerisation, cloud automation, and deployment pipelines.
• Strong understanding of observability, reliability engineering, monitoring, logging, tracing, and operational excellence.
• Experience designing secure and resilient cloud-native solutions.
• Understanding of scalability, performance optimisation, cloud security, governance, and cost management.
• Ability to design, build, deploy, and operate production-grade workloads in cloud environments.

• Experience applying application security principles and secure engineering practices.
• Understanding of identity management, access control, encryption, threat modelling, and secure architecture.
• Experience implementing DevSecOps and automated governance controls.
• Ability to balance security, compliance, risk, usability, and delivery speed.

• Hands-on experience using AI-assisted engineering tools within software delivery workflows.
• Understanding of agentic architectures, automation frameworks, tool orchestration, and human-in-the-loop systems.
• Knowledge of responsible AI practices, governance, and risk management.

• Demonstrated senior-level technical leadership.
• Strong communication and stakeholder management skills.
• Ability to influence business leaders, architects, engineers, and delivery teams.
• Proven ability to raise the capability of teams around you.
• Comfortable making difficult decisions and owning outcomes.

Success in this role is demonstrated through:

• Clearly defined and measurable business outcomes.
• Production-ready capabilities delivered within agreed timeframes.
• Increased engineering velocity through modern engineering and AI-assisted practices.
• Reusable enterprise capabilities and engineering patterns.
• Secure, resilient, and supportable solutions.
• Improved adoption and operational sustainability.
• Increased engineering capability across teams.
• Reduced complexity, technical debt, and operational risk.
• Successful transition of ownership to permanent teams.

• Not a pure Enterprise Architect.
• Not a traditional Software Engineer.
• Not a Consultant.
• Not a Project Manager.
• Not a Permanent Product Team Owner.
• Not an AI Tool Specialist.
• Not an Individual Hero.

Focus: Business Outcomes, Engineering Execution, Cloud Engineering, Secure-by-Design Delivery, AI-Assisted Engineering, and Organisational Enablement.

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