Job Description: Role: Senior Software engineer
Atlanta-GA
Onsite
Top skills required for this role:
Frontend: TypeScript React and modern CSS frameworks (e.g. Tailwind CSS).
Backend: (Express/NestJS) and/or Python (FastAPI/Django).
Databases: Relational databases (PostgreSQL/AWS Aurora) and Vector Data Stores (Amazon OpenSearch Serverless pgvector or Pinecone).
Cloud Infrastructure (AWS or GCP): Deep familiarity with AWS services (Lambda ECS/EKS S3 API Gateway) and Infrastructure as Code (Terraform or AWS CDK).Cloud Infrastructure (AWS or GCP): Deep familiarity with AWS services
(Lambda ECS/EKS S3 API Gateway DynamoDB) and GCP services (Cloud Functions GKE Cloud Storage Cloud Pub/Sub) and Infrastructure as Code (Terraform or AWS CDK/GCP Deployment Manager).
AI & Orchestration: Amazon Bedrock (interacting with Foundation Models like Claude or Llama) Google Vertex AI LangChain/LlamaIndex and RAG architectures.
DevOps & CI/CD: Docker GitLab CI and observability tools (Datadog AWS CloudWatch Google Cloud Monitoring Google Cloud Logging).
Testing: Automated testing frameworks across the stack (Jest PyTest Playwright or Cypress).
Developer Tools: Advanced proficiency with AI coding assistants (Cursor GitLab Duo Claude Code).
Job Description/ Responsibilities
Platform & App Development: Architect build and scale end-to-end applications. You will take ownership of major platform features ensuring they are performant scalable and resilient.
Frontend Engineering: Build responsive highly interactive and accessible user interfaces. You will manage complex global state and optimize frontend performance.
Backend Engineering: Design and implement robust RESTful and GraphQL APIs. You will architect microservices or modular monoliths that can handle high-throughput enterprise traffic.
Modern DevOps & CI/CD: Design and maintain automated CI/CD pipelines. You will enforce strict automated testing containerization and deployment strategies (Blue/Green Canary) to ensure AI-assisted code is safely tested and deployed.
Enterprise AI Integration (AWS Bedrock or Vertex etc): Integrate LLMs into our platform using Amazon Bedrock or Vertex. You will build highly secure RAG pipelines manage vector databases and implement AI features that directly drive user value.
AI-Assisted Engineering: Utilize tools like Cursor or GitHub Copilot to accelerate the generation of boilerplate and standard logic while focusing your human effort on platform architecture code review and system design.