Senior Backend Engineer

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 Mongo

DB 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 (Graph

QL and JSON‑RPC).

Experience with Graph

QL 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 data pipelines to keep data flowing reliably from ingestion to delivery.
• Build 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 Mongo
  
  DB schemas and query/index strategies for scale (aggregation pipelines, Atlas Search/vector search where relevant).
• JSON‑RPC data layer service that powers Graph
  
  QL (designing/maintaining RPC methods, scaling throughput/latency, evolving contracts safely).

• Own backend services end‑to‑end, from design and implementation through deployment and production support.
• Deliver scalable Graph
  
  QL resolvers with performance‑aware patterns (batching, caching, pagination).
• Build and evolve the JSON‑RPC data access layer for Graph
  
  QL, including method design, backward compatibility, and performance tuning.
• Own Mongo
  
  DB performance: modeling, indexing, aggregation pipelines, and measurable latency/throughput targets.
• Improve reliability and operational readiness (SLO‑mindful 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 Graph
  
  QL APIs (schema design, resolver patterns, authorization, performance).
• Strong experience with Mongo
  
  DB (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, 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 integrating with…