DescriptionWhat is your mission?We are seeking a talented and driven developer to join our team and help build scalable, cloud-native solutions on AWS. This role sits at the intersection of modern software engineering and applied AI - leveraging AI-powered development tools to enhance engineering productivity while also designing and delivering AI-driven capabilities within our product.
You will be developing and maintaining a product to transform complex forensic data from diverse sources into meaningful investigative leads and intelligence insights. This platform will analyze large-scale datasets to uncover patterns, anomalies, and relationships across entities - enabling users to see the bigger picture and act with confidence.
Responsibilities:- Design and implement features for our AWS-based AI solution, including job orchestration, manifest generation, and API integrations.
- Build and maintain AI-powered product features that extract insights from complex, multi-source forensic datasets.
- Leverage AI-assisted development tools to accelerate delivery while maintaining high standards of quality, security, and performance.
- Ensure code quality and reliability through comprehensive unit testing, performance testing, and observability enhancements.
- Collaborate with cross-functional teams to integrate AI services into broader investigation workflows and runtime environments.
- Troubleshoot deployment issues and contribute to the continuous improvement of dashboards and monitoring tools.
- Contribute to data modelling, algorithm exploration, and proof-of-concept development.
Requirements: - Strong proficiency in Python and TypeScript, with experience building APIs and cloud-native architecture.
- Knowledge of Node.js, Next.js, and React preferred; experience with Go is a plus.
- Hands-on experience with AWS services such as SageMaker and Lambda.
- Ability to produce clear, maintainable code and contribute to shared documentation and engineering standards.
- Working knowledge of GitHub workflows, secrets handling, and secure release practices.
- Experience with Amazon EKS and Kubernetes-based deployment environments.
- Exposure to GenAI platforms, including LLM integration and semantic caching techniques.
- Familiarity with observability platforms such as Datadog and structured log aggregation.
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