Daman is seeking a versatile and experienced Senior Full Stack Engineer to build critical infrastructure, APIs, and user interfaces that connect advanced machine learning models with end-users. The role involves leveraging a modern AWS stack to deliver scalable, secure, and performant AI-driven applications within a regulated financial services environment.
Responsibilities:
- Design & Build: Develop scalable, event-driven backend architectures using AWS Lambda, API Gateway, and Step Functions
- Data Integration: Write efficient queries and build data access layers for both relational (Amazon Aurora) and graph databases (Amazon Neptune/Neptune Analytics)
- ML Operationalization: Partner closely with Data Scientists to integrate, deploy, and monitor machine learning models into production applications
- Frontend Development: Build intuitive, high-performance web interfaces (using React/TypeScript) that allow analysts to visualize complex data graphs and consume ML insights
- Infrastructure & Security: Provision cloud resources using Infrastructure as Code (IaC) and ensure all systems adhere to strict financial compliance, security, and data privacy standards
Requirements:
- 4+ years of hands-on experience with AWS, specifically building event-driven applications using AWS Lambda, SQS/SNS, and API Gateway
- Strong proficiency in Python (for ML integration/data scripting) and TypeScript/Node.js (for scalable backend services)
- Solid experience with relational databases, specifically Amazon Aurora (PostgreSQL preferred) and complex SQL optimization
- Proven experience with Graph Databases (Amazon Neptune, Neo4j, etc.) and graph query languages such as openCypher or Gremlin
- 3+ years of experience building responsive, user-facing applications using React
- Experience with Infrastructure as Code (Terraform, AWS CDK, or CloudFormation) and a strong understanding of cloud security principles (IAM, KMS) within a highly regulated industry
- Familiarity with Amazon SageMaker, ML model deployment, or container orchestration (Docker/ECS/EKS)
- Experience utilizing vector databases (e.g., pgvector), LLMs via Amazon Bedrock, or frameworks like LangChain/LlamaIndex
- Experience with graph visualization libraries (e.g., Cytoscape.js, react-force-graph, or D3.js) to render complex network structures in the browser
- Previous experience working in fintech, banking, with an understanding of use cases like fraud detection, entity resolution, or risk modeling