Retail Platform Principal Engineer

• 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 Morden 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 prioritized 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.

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 prioritized 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

Familiar on one or more tech skill.

• 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.