Scaling a Video-Based Recruitment Platform with Serverless Architecture
Improving performance & resilience with optimized serverless and event-driven architecture
Consistent performance under peak load conditions
by optimizing serverless concurrency and event-driven processing
Stronger system resilience and failure isolation
with buffered workloads, DLQs, and improved API governance
About the Client
A human resources technology company offering a video-first recruitment platform that modernizes hiring through short-form candidate profiles and swipe-based interactions. The platform helps employers assess candidates holistically, improving hiring efficiency, engagement, inclusivity, and long-term employee retention.
Industry
Human Resources Technology
Years in business
3+ years
Employee count
10+
Presence
United States
“Forgeahead helped us modernize our cloud infrastructure in a way that improved scalability, reliability, and overall efficiency for our research platform.”
Dr. Michael Harrington
Chief Information Officer
the need
A US-based video-first hiring platform experienced scalability and operational challenges as usage grew.
Its serverless architecture, built on AWS Lambda and Amazon SQS, struggled with fluctuating workloads, which led to inconsistent background processing performance. Limited observability across distributed components made monitoring and troubleshooting difficult, while Lambda cold starts increased latency and impacted user experience.
As the platform scaled, improving system reliability, visibility, and cost efficiency became critical.
the solution
Forgeahead implemented a serverless optimization strategy focused on improving system reliability, scalability, and observability through structured event handling and enhanced API governance.
End-to-end request traceability was strengthened by introducing correlation IDs and transaction identifiers, enabling precise tracking of requests across services and significantly improving root cause analysis. Traffic management was optimized through controlled rate handling and load testing, ensuring stable performance under both steady-state and peak load conditions.
To manage workload spikes effectively, intelligent request throttling and buffering mechanisms were introduced at the API level. Amazon SQS and Amazon Kinesis were used to buffer traffic surges, preventing downstream system overload. Lambda concurrency was also optimized to enable efficient parallel processing, while dead-letter queues (DLQs) ensured failed transactions were captured, retried, and handled without impacting system stability.
At the API layer, strict input validation and schema enforcement improved data integrity by preventing invalid requests from entering the system. Additionally, API governance mechanisms were implemented using usage plans, quotas, and key-based access controls, ensuring controlled consumption and enabling flexible tier-based access for different users.
The Impact
Improved Scalability and Reliability
Enabled consistent performance under fluctuating demand through improved traffic handling and concurrency control.
Stronger System Resilience
Reduced the risk of cascading failures with buffering mechanisms and fault-tolerant design.
Enhanced Observability
Improved visibility into system behavior through structured logging and monitoring.
Reduced Failure Impact
Isolated and managed failed transactions using DLQs, minimizing disruption to core workflows.
Higher Data Integrity
Prevented invalid data entry through API-level validation and schema enforcement.
Optimized Cost Efficiency
Improved resource utilization through controlled scaling and efficient serverless execution.
Role of AWS
AWS services played a central role in enabling event-driven scalability and operational resilience. AWS Lambda, Amazon SQS, and Amazon Kinesis formed the backbone of asynchronous processing, allowing the system to scale automatically based on demand.
Amazon API Gateway enforced API governance through throttling, usage limits, and request validation, ensuring secure and controlled API consumption. Amazon CloudWatch provided real-time monitoring and performance insights, enabling proactive issue detection and resolution.
Security and compliance were strengthened through encryption at rest and in transit, along with controlled API access mechanisms. Additionally, AWS Cost Explorer and anomaly detection tools provided visibility into usage patterns, enabling proactive cost optimization and governance.
Tech Stack
The platform was built on a cloud-native serverless architecture using AWS Lambda, Amazon SQS, and Amazon Kinesis for event-driven processing. API management was handled through Amazon API Gateway, while Amazon CloudWatch provided monitoring and observability. The core application logic was developed using Python.