Senior Software Engineer

About role

Join our Shared Product Team as a Software Engineer and make a direct impact on every client and investor relationship across our organization. You will build the integration layer for our KYC (Know Your Customer) and AML operations. This layer lets internal applications and teams securely access and update investor information.

You will be responsible for creating the critical "integration points" that allow various internal applications and teams to seamlessly access and update investor information. This role sits at the heart of our data strategy, ensuring that high-quality investor data is secure, scalable, and readily available across our global ecosystem.

Roles & Responsibilities

• Lead the integration of Fenergo APIs to extract, synchronise and publish KYC and AML data across internal platforms.

• Design, build and run secure, high-performance REST and GraphQL APIs. These APIs are the main way that applications reach the investor data store.

• Build and operate a gateway layer that hides vendor API complexity. Handle rate limits, retries, idempotency, pagination and schema changes.

• Apply strong authentication and authorisation to every API: OAuth2 and OIDC, role-based and attribute-based access control, fine-grained authorisation, tenant isolation and audit logging.

• Build Python pipelines for document ingestion, crawling, parsing, transformation and synchronisation across platforms. Use event-driven and batch patterns.

• Build search and retrieval features for client documents. Use text extraction, embeddings and vector search. Apply LLM-assisted classification and extraction with human review and clear evaluation criteria.

• Contribute to the design and delivery of a KYC tokenisation proof of concept. This covers verifiable credentials, consent management and ledger-based sharing of KYC data.

• Design relational (SQL) and non-relational data models for complex investor and entity data at scale.

• Build React user interfaces, such as review and operations screens.

• Add validation, monitoring and alerting so investor data stays accurate and available. Use metrics, logs and distributed tracing.

• Run services in containers on Kubernetes. Deploy through CI/CD pipelines and infrastructure as code.

• Take part in requirements analysis. Turn business needs into technical designs. Improve code quality through peer review and automated tests.

• Support training and user adoption across global teams.

Skills & Qualifications Required

• BSc, BE or BTech in Computer Science, Engineering or a related field.

• 7+ years in software engineering, with a focus on financial systems or other regulated domains.

• Strong Python for backend services and data processing. Experience with FastAPI or a similar framework, and with async programming.

• Experience building secured REST APIs with high performance and security requirements. GraphQL is a plus.

• Solid knowledge of OAuth2, OIDC, JWT and access control models. Experience with audit logging.

• Strong SQL and database skills, preferably PostgreSQL. Experience with schema design, migrations and query tuning for financial data.

• Experience with NoSQL stores or document stores.

• Strong grounding in software architecture, design patterns, microservices and event-driven design.

• Experience with React and TypeScript.

• Experience with Docker and Kubernetes.

• Experience deploying on AWS. Familiarity with infrastructure as code, such as Terraform.

• Knowledge of secure SDLC practice for financial applications.

• Understanding of observability, alerting and incident response. Experience with OpenTelemetry or similar tools.

• Strong test automation skills: unit, integration and contract tests.

Preferred

• Experience integrating with KYC, AML or client lifecycle management platforms, such as Fenergo.

• Knowledge of KYC and AML concepts: client onboarding, periodic review, beneficial ownership, sanctions and adverse media screening.

• Experience with document processing: OCR, parsing, chunking, embeddings and vector databases such as pgvector.

• Experience with LLM-based applications: retrieval-augmented generation, prompt design, evaluation and cost control.

• Experience with crawling and scheduled ingestion jobs.

• Experience with message queues and event streaming.

• Exposure to blockchain or distributed ledger technology, verifiable credentials or decentralised identity.

• Experience with data platforms and pipelines, such as Databricks or Spark.