Principal Software Development Engineer

Job Summary

Principal Full-Stack Engineer Customer Success Support

Responsibilities

• Architect and ship full-stack solutions (React/Next.js + TypeScript frontends; Node/Python/Go/Java backends) and production APIs.
• Design cloud architectures on public cloud: managed DBs, object storage, networking, IAM, and secure service-to-service patterns.
• Build data platforms: relational + NoSQL, caching, search, event streams, and object storage; enable vector/semantic stores for AI.
• Own CI/CD and IaC (Terraform / ARM / Bicep, GitHub Actions / Azure DevOps / Jenkins); containerize and run on Kubernetes (AKS/EKS).
• Instrument observability and SLAs (metrics, tracing, logging); ensure operational excellence and security/compliance.
• Integrate with enterprise platforms (Salesforce, Coveo/search, identity providers) and partner with Product, Support Ops, Security, and ML teams.
• Mentor engineers and introduce standards, templates, and automation to scale delivery.

Qualifications

• 10+ years building full-stack systems at scale.
• Expert at using variety of AI tools and stacks to solve enterprise grade problems
• Strong frontend (React/Next.js + TypeScript) and backend (Node/Python/Go/Java) experience.
• Deep Azure and/or AWS experience: compute, networking, managed databases (Postgres/Aurora/DynamoDB/Cosmos), S3/Azure Blob.
• Hands-on CI/CD and IaC (Terraform / ARM / Bicep; GitHub Actions / Azure DevOps / Jenkins).
• Containerization orchestration: Docker + Kubernetes (AKS/EKS).
• Data platform familiarity: caching (Redis), search (Elasticsearch/Coveo), eventing (Kafka/Kinesis), object storage.
• Strong observability, security, and production-ops experience.
• Excellent communicator who translates product/customer needs into robust technical solutions.
• Leverages AI tools to boost team productivity uses AI-assisted development and automation to create multiplier effects across the org.
• Built predictive generative AI apps in production and can guide product requirements to embed AI features (RAG, embeddings/vector stores, predictive pipelines) into workflows.
• BS/MS in CS or equivalent experience.

Nice to have

• Prior experience with customer success / support systems (portal, knowledge).
• Familiarity with LLM integration patterns and vector DBs (FAISS, Milvus).