Senior Full Stack Engineer (MLOps & Data Platform)

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