We are looking for a talented and driven developer to join our Research Engineering team and contribute to the development of scalable, cloud-native AI solutions on AWS. This role focuses on building and maintaining infrastructure and services that support advanced machine learning workflows, real-time inference, and robust observability.

 

What is your mission?

Research and build data-driven applications Research and develop Machine Learning algorithms to turn forensic data into investigation leads, evidence, and intelligence insights Be a part of a heterogeneous, multi-disciplinary research team Take projects end to end, from the inception of the idea, through data and algorithm research and POC development The work is both analytical and experimental in nature and includes prototyping of new algorithms, investigating data, and evaluating algorithm performance

 

Responsibilities

Design and implement features for our AWS-based AI solution, including job orchestration, manifest generation, and API integrations Develop and maintain CI/CD pipelines using GitHub Actions, CDK, and AWS infrastructure (e.g., SageMaker, Lambda, Bedrock) 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

 

Required Skills

Proficiency in Python and Typescript, with experience in API development and cloud-native architectures Hands-on experience with AWS services including SageMaker, Lambda, etc. Experience with machine learning libraries such as PyTorch, Pandas, NumPy, and related data processing tools Ability to write clean, maintainable code and contribute to shared documentation and best practices Familiarity with GitHub workflows, secrets management, and secure deployment practices Familiarity with Amazon EKS and Kubernetes-based deployments

Preferred Qualifications

Experience with GenAI infrastructure, semantic caching, and LLM integration Knowledge of observability tools like Datadog and log aggregation strategies Prior contributions to scalable AI platforms or high-throughput batch processing systems