Senior Backend Engineer

Senior Backend Engineer – GenAI Lens Backend Platform

We’re looking for a Senior Backend Engineer to build and scale the GenAI Lens backend platform. This is a production-focused role centered on MongoDB data design and performance, scalable/durable data ingestion and processing pipelines, and operating high-throughput systems with strong observability and reliability.

You’ll also help build and evolve our API layer (GraphQL and JSON-RPC). Experience with GraphQL is a strong plus, but the core focus is data systems and platform engineering. You should be comfortable working in a product that leverages modern AI technologies (embeddings, vector search, LLM integrations) and understand the practical standards that come with them—evaluation, guardrails, observability, and cost controls.

• Maintain and improve our data pipelines to keep data flowing reliably from ingestion to delivery
• Scalable, durable data ingestion and processing pipelines (event-driven, fault-tolerant workflows; retries, idempotency, backfills, and DLQs)
• Own data quality by implementing monitoring, alerting, and validation
• Design MongoDB schemas and query/index strategies for scale (aggregation pipelines, Atlas Search/vector search where relevant)
• JSON-RPC data layer service that powers GraphQL (designing/maintaining RPC methods, scaling throughput/latency, and evolving contracts safely)

• Own backend services end-to-end, from design and implementation through deployment and production support
• Deliver scalable GraphQL resolvers with performance-aware patterns (batching, caching, pagination)
• Build and evolve the JSON-RPC data access layer for GraphQL, including method design, backward compatibility, and performance tuning.
• Own MongoDB performance: modeling, indexing, aggregation pipelines, and measurable latency/throughput targets
• Improve reliability and operational readiness (SLO-minded engineering, incident response hygiene, runbooks)
• Partner closely with frontend, product, and UX to enable features cleanly and safely
• Drive code quality via testing, reviews, and CI/CD improvements
• Mentor engineers and influence engineering standards across the team

• Experience building and maintaining backend services in production
• Strong experience with GraphQL APIs (schema design, resolver patterns, authorization, performance)
• Strong experience with MongoDB (data modeling, indexing, aggregation pipelines; Atlas Search and Vector Search, sharding experience is a plus)
• Strong proficiency with Python (services, jobs, data processing, or tooling)
• Experience building on AWS, preferably serverless/event-driven architectures (Lambda, SQS/SNS/Event Bridge, S3)
• Experience working with high-traffic or business-critical systems
• Solid understanding of performance optimization techniques (caching, async processing, data access patterns)
• Experience with Terraform for production infrastructure (IAM, networking, secrets, repeatable environments)
• Experience with testing frameworks and pragmatic test strategy (unit, integration, contract)
• Familiarity with CI/CD pipelines (Git Hub Actions preferred; alternatives acceptable)
• Experience debugging, monitoring, and operating production systems
• Comfortable working in a distributed team environment
• Strong operational skills: logging, metrics, tracing, dashboards/alerts, and production support practices

• Cloud experience, preferably AWS, including:
  • Lambda
  • SQS / SNS / Event Bridge / Kafka
  • S3
  • Cloud Watch
  • API Gateway (where applicable)
  • Experience with Infrastructure as Code, preferably Terraform
  • Experience with private networking patterns (VPC, security groups, Private Link/VPC endpoints) is a plus
  • Exposure to containerized workloads (EKS/Kubernetes) is a plus, even if the core architecture is serverless-first

• Prior experience leading projects, features, or technical initiatives
• Ability to influence architecture and design decisions
• Strong ownership mindset and ability to identify and execute improvements independently
• Familiarity with embeddings and vector search concepts, and the tradeoffs they introduce (latency, cost, relevance)
• Experience…