Backend Engineer (Node.js/Python)
Key Responsibilities
Must Have Required Skills
Preferred Qualifications
Must Have Required Skills
Preferred Qualifications
Must Have Required Skills
Preferred Qualifications
- Design and build RESTful and event-driven backend services in Python and/or Node.js that support multi-agent orchestration, HITL workflows, and exception routing across finance processes.
- Architect and implement the workflow state management layer — handling execution context persistence, checkpointing, idempotent retries, and safe resume for long-running finance workflows using AWS Step Functions or equivalent.
- Implement event-driven architecture using AWS SQS, SNS, and EventBridge to decouple platform services and support asynchronous, high-throughput financial document processing.
- Develop and maintain APIs that expose platform capabilities (exception management, approval routing, notification triggers, audit trail) to downstream consumers and front-end applications.
- Ensure platform observability through structured logging, distributed tracing (AWS X-Ray), and CloudWatch dashboards — with alerting on SLA breaches, queue depth, and service health.
- Collaborate with AI engineers to integrate LLM-based services and agent outputs into backend workflows, handling model responses, confidence thresholds, and fallback routing.
- Implement security best practices — IAM role-based access, secrets management via AWS Secrets Manager, data encryption at rest and in transit, and audit logging for compliance.
- 5+ years of backend engineering experience with strong proficiency in Python and/or Node.js.
- Solid experience building and deploying microservices on AWS — Lambda, ECS/Fargate, API Gateway, SQS, SNS, EventBridge, Step Functions, and RDS/Aurora.
- Strong understanding of event-driven and async architecture patterns for distributed systems.
- Experience designing RESTful APIs and working with message queues for decoupled, high-throughput processing.
- Hands-on experience with relational databases (PostgreSQL, MySQL) and NoSQL stores (DynamoDB) — including schema design, indexing, and query optimisation.
- Experience with Infrastructure as Code — AWS CDK, Terraform, or CloudFormation.
- Solid understanding of multi-tenancy patterns — tenant isolation, per-tenant configuration, and shared service design.
- Experience with workflow orchestration services — AWS Step Functions, Apache Airflow, or Temporal — for managing long-running, stateful processes.
- Strong grasp of observability fundamentals — structured logging, distributed tracing, metrics, and alerting in production environments.
- Experience with CI/CD pipelines (GitHub Actions, AWS CodePipeline, or equivalent) and container-based deployments (Docker, ECS).
- Exposure to Finance and Accounting processes (P2P, O2C, R2R) or prior work in a BPO or shared services technology context.
- Knowledge of financial compliance requirements — audit trail design, SOX control considerations, or data retention policies.
- Familiarity with vector databases (pgvector, Pinecone) and basic understanding of how LLM-based services are consumed from backend APIs.
- Experience with AWS Textract or similar document processing services for structured extraction from financial documents.
- Knowledge of API security patterns — OAuth 2.0, JWT, mTLS — relevant to enterprise ERP and banking system integrations.
- Understanding of anomaly detection pipelines in transactional data contexts.
- Experience with human-in-the-loop (HITL) workflow patterns — task queuing, approval routing, SLA escalation, and decision capture.
- Design and build RESTful and event-driven backend services in Python and/or Node.js that support multi-agent orchestration, HITL workflows, and exception routing across finance processes.
- Architect and implement the workflow state management layer — handling execution context persistence, checkpointing, idempotent retries, and safe resume for long-running finance workflows using AWS Step Functions or equivalent.
- Implement event-driven architecture using AWS SQS, SNS, and EventBridge to decouple platform services and support asynchronous, high-throughput financial document processing.
- Develop and maintain APIs that expose platform capabilities (exception management, approval routing, notification triggers, audit trail) to downstream consumers and front-end applications.
- Ensure platform observability through structured logging, distributed tracing (AWS X-Ray), and CloudWatch dashboards — with alerting on SLA breaches, queue depth, and service health.
- Collaborate with AI engineers to integrate LLM-based services and agent outputs into backend workflows, handling model responses, confidence thresholds, and fallback routing.
- Implement security best practices — IAM role-based access, secrets management via AWS Secrets Manager, data encryption at rest and in transit, and audit logging for compliance.
- 5+ years of backend engineering experience with strong proficiency in Python and/or Node.js.
- Solid experience building and deploying microservices on AWS — Lambda, ECS/Fargate, API Gateway, SQS, SNS, EventBridge, Step Functions, and RDS/Aurora.
- Strong understanding of event-driven and async architecture patterns for distributed systems.
- Experience designing RESTful APIs and working with message queues for decoupled, high-throughput processing.
- Hands-on experience with relational databases (PostgreSQL, MySQL) and NoSQL stores (DynamoDB) — including schema design, indexing, and query optimisation.
- Experience with Infrastructure as Code — AWS CDK, Terraform, or CloudFormation.
- Solid understanding of multi-tenancy patterns — tenant isolation, per-tenant configuration, and shared service design.
- Experience with workflow orchestration services — AWS Step Functions, Apache Airflow, or Temporal — for managing long-running, stateful processes.
- Strong grasp of observability fundamentals — structured logging, distributed tracing, metrics, and alerting in production environments.
- Experience with CI/CD pipelines (GitHub Actions, AWS CodePipeline, or equivalent) and container-based deployments (Docker, ECS).
- Exposure to Finance and Accounting processes (P2P, O2C, R2R) or prior work in a BPO or shared services technology context.
- Knowledge of financial compliance requirements — audit trail design, SOX control considerations, or data retention policies.
- Familiarity with vector databases (pgvector, Pinecone) and basic understanding of how LLM-based services are consumed from backend APIs.
- Experience with AWS Textract or similar document processing services for structured extraction from financial documents.
- Knowledge of API security patterns — OAuth 2.0, JWT, mTLS — relevant to enterprise ERP and banking system integrations.
- Understanding of anomaly detection pipelines in transactional data contexts.
- Experience with human-in-the-loop (HITL) workflow patterns — task queuing, approval routing, SLA escalation, and decision capture.
- Design and build RESTful and event-driven backend services in Python and/or Node.js that support multi-agent orchestration, HITL workflows, and exception routing across finance processes.
- Architect and implement the workflow state management layer — handling execution context persistence, checkpointing, idempotent retries, and safe resume for long-running finance workflows using AWS Step Functions or equivalent.
- Implement event-driven architecture using AWS SQS, SNS, and EventBridge to decouple platform services and support asynchronous, high-throughput financial document processing.
- Develop and maintain APIs that expose platform capabilities (exception management, approval routing, notification triggers, audit trail) to downstream consumers and front-end applications.
- Ensure platform observability through structured logging, distributed tracing (AWS X-Ray), and CloudWatch dashboards — with alerting on SLA breaches, queue depth, and service health.
- Collaborate with AI engineers to integrate LLM-based services and agent outputs into backend workflows, handling model responses, confidence thresholds, and fallback routing.
- Implement security best practices — IAM role-based access, secrets management via AWS Secrets Manager, data encryption at rest and in transit, and audit logging for compliance.
- 5+ years of backend engineering experience with strong proficiency in Python and/or Node.js.
- Solid experience building and deploying microservices on AWS — Lambda, ECS/Fargate, API Gateway, SQS, SNS, EventBridge, Step Functions, and RDS/Aurora.
- Strong understanding of event-driven and async architecture patterns for distributed systems.
- Experience designing RESTful APIs and working with message queues for decoupled, high-throughput processing.
- Hands-on experience with relational databases (PostgreSQL, MySQL) and NoSQL stores (DynamoDB) — including schema design, indexing, and query optimisation.
- Experience with Infrastructure as Code — AWS CDK, Terraform, or CloudFormation.
- Solid understanding of multi-tenancy patterns — tenant isolation, per-tenant configuration, and shared service design.
- Experience with workflow orchestration services — AWS Step Functions, Apache Airflow, or Temporal — for managing long-running, stateful processes.
- Strong grasp of observability fundamentals — structured logging, distributed tracing, metrics, and alerting in production environments.
- Experience with CI/CD pipelines (GitHub Actions, AWS CodePipeline, or equivalent) and container-based deployments (Docker, ECS).
- Exposure to Finance and Accounting processes (P2P, O2C, R2R) or prior work in a BPO or shared services technology context.
- Knowledge of financial compliance requirements — audit trail design, SOX control considerations, or data retention policies.
- Familiarity with vector databases (pgvector, Pinecone) and basic understanding of how LLM-based services are consumed from backend APIs.
- Experience with AWS Textract or similar document processing services for structured extraction from financial documents.
- Knowledge of API security patterns — OAuth 2.0, JWT, mTLS — relevant to enterprise ERP and banking system integrations.
- Understanding of anomaly detection pipelines in transactional data contexts.
- Experience with human-in-the-loop (HITL) workflow patterns — task queuing, approval routing, SLA escalation, and decision capture.