Retail Platform Principal Engineer

We are currently seeking an experienced professional to join our team in the role of Retail Platform Principal Engineer in WPS Technology.

Responsibilities:

• Provide strategic technical leadership across engineering projects, ensuring alignment with business goals, architecture principles, and regulatory/compliance requirements.
• Architect and design scalable, resilient, secure microservices and event-driven systems to support high-throughput financial services workloads.
• Lead adoption and governance of engineering standards, patterns, and best practices (API design, domain-driven design, CI/CD).
• Hands-on development and code review in core platforms; set standards for software craftsmanship across Modern Tech stacks and cloud-native services.
• Mentor and coach senior engineers and engineering leads; run regular technical deep dives, architecture reviews, and cross-team workshops.
• Lead and facilitate blameless post-mortems and structured root cause analysis (RCA), driving remediation plans and lessons learned into the development lifecycle.
• Own the Tech Risk Control 'book of work': maintain backlog of control remediation tasks, coordinate with Risk/Compliance to define mitigations, track progress, and ensure audit readiness.
• Drive the integration of AI/ML capabilities into production systems with appropriate controls for governance, model risk, explainability, and data privacy.
• Collaborate with product, security, ops, and data teams to prioritise technical debt, reliability improvements, and platform investments.
• Represent engineering in stakeholder forums, technical governance boards, and external partner engagements.

What you will be doing:

• Act as the technical owner for core platform domains, driving architecture, design, and delivery across backend, data streaming, and cloud infrastructure; focus deeply on 2-3 areas (e.g., Java/Spring, Kafka/Stream Processing, Cloud/Kubernetes).
• Lead targeted technical deep-dives (2-3 per quarter) to resolve architectural trade-offs, eliminate chronic pain points, and transfer knowledge across teams.
• Own and deliver the Tech Risk Control book-of-work items for your area: define remediations, coordinate cross-functional delivery, produce evidence for audits, and close out controls.
• Run structured root-cause analysis and blameless post-mortems for major incidents, translate findings into prioritised fixes, and ensure follow-through.
• Provide hands-on technical direction: participate in design reviews, code reviews, prototyping, and proof-of-concepts to validate architecture decisions.
• Mentor senior engineers and engineering leads; raise team capability through coaching, workshops, and documented standards/patterns.
• Collaborate with product, security, data, and ops to align technical strategy with business objectives, regulatory requirements, and non-functional goals (scalability, resilience, security).
• Drive adoption of cloud-native and DevOps practices (CI/CD, observability) and ensure production readiness for new services and AI/ML components.

Qualifications Certifications & Education:

• University degree in Computer Science, Engineering, or related discipline; MSc/PhD preferred.
• Cloud certification(s) strongly preferred: AWS Certified Solutions Architect Professional or DevOps Engineer, Microsoft Certified: Azure Solutions Architect, or Google Professional Cloud Architect.
• Desirable: Kubernetes (CKA/CKAD), security or data governance certificates.

Preferred Qualifications:

• Experience in financial services or regulated industries with demonstrable knowledge of regulatory controls, audit preparation, and model risk governance.
• Contributions to open source projects or academic/industry research; speaker at technical conferences is a plus.
• Proven ability to lead cross-functional engineering teams through technological transformations, and to operationalise AI safely at scale.

Deliverables & Success Measures:

• Architecture and platform designs that meet non-functional requirements (scalability, resiliency, security).
• Successful delivery of prioritised Tech Risk Control book-of-work items and remediation actions.
• Reduction in incident mean-time-to-detect and mean-time-to-recover through improved observability, RCA, and runbooks.
• Mature AI/ML deployment pipelines with documented governance, monitoring, and model lifecycle controls.
• High team engagement, evidence of knowledge transfer via deep dive sessions, and measurable uplift in engineering capability.

Technical Skills & Experience:

• Strong Java expertise (Java 8/11/17/21/25), demonstrated through large-scale, production systems.
• Deep experience with Spring ecosystem: Spring Boot, Spring Cloud, Spring Security, Spring Data, and related tooling.
• Microservices and cloud-native architectures: containerisation (Docker), orchestration (Kubernetes), service mesh (Istio/Linkerd), and 12-factor app principles.
• Event streaming and messaging: Apache Kafka (Kafka Streams, ksqlDB), Confluent tooling, and best practices for exactly-once semantics and fault-tolerant processing.
• Data platform familiarity: batch/stream processing (Spark, Flink), data lakehouse concepts (Delta Lake), and data governance tools (e.g., Apache Atlas, Apache Falcon, or equivalent).
• AI/ML/LLM productionisation: hands-on with MLOps and Model frameworks (LangGraph, Agent Development Kit, Microsoft Agent Framework); experience deploying and operating LLMs (e.g., Falcon family or other LLMs), embeddings, RAG architectures, and frameworks (LangChain, LlamaIndex).
• Observability, reliability, and performance engineering: Prometheus/Grafana, ELK/EFK, distributed tracing (Jaeger/Zipkin), profiling, and tuning high-throughput systems.
• DevOps, CI/CD: Terraform, Ansible, G3, and SonarQube.
• Cloud platforms: proven experience on at least one major public cloud (AWS, Azure, or GCP) in production-scale environments.
• Security, compliance & risk controls: secure coding practices, threat modelling, encryption, key management, and OWASP mitigation patterns.