Senior Software engineer

Job Title

Top skills required for this role: Frontend: TypeScript, React, Next.js, and modern CSS frameworks (e.g., Tailwind CSS). Backend: Node.js (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, 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.