Software Development Engineer III (CRM - Opportunities - AI)

What you will do:

• Own the full lifecycle of the Opportunities backend: translate business and customer needs into scalable, production-grade services
• Design and evolve the data model for pipelines, stages, deals, and associated entities — getting consistency, throughput, and query patterns right
• Build and scale APIs, event-driven workflows, and background jobs that handle high-volume writes, automations, and integrations
• Tackle distributed systems concerns: idempotency, eventual consistency, race conditions, hot tenants, and noisy-neighbor isolation
• Improve performance and reliability of large pipelines (millions of opportunities per workspace) through indexing, sharding, caching, and query optimization
• Build the AI-assisted surfaces of Opportunities on top of existing AI capabilities — context assembly from CRM data, prompt and response contracts, caching, cost and latency budgets, graceful degradation when a call is slow, wrong, or unavailable, and a feedback loop for measuring output quality
• Ship end-to-end when needed — including UI changes in Vue — without blocking on cross-functional handoffs
• Instrument the system: logs, metrics, traces, and SLOs that make production behavior legible
• Drive incident response, postmortems, and the engineering hygiene that keeps a high-traffic product healthy

What you will need:

• 4+ years building and operating production backend systems at scale
• Strong fundamentals in data modeling (SQL and NoSQL), API design, and distributed systems
• Hands-on experience with Node.js or Go in production
• Track record of owning services end-to-end — design, build, deploy, on-call
• Comfort working independently in ambiguous problem spaces with high ownership
• Solid grasp of scalability, performance, and reliability trade-offs

Preferred Qualifications:

• Experience building CRM, sales, pipeline, or workflow products
• Worked on multi-tenant SaaS at scale (sharding, tenant isolation, fair-use limits)
• Familiarity with event streaming (Kafka/Redis Streams), queues, and async processing
• Exposure to MongoDB, Postgres, ElasticSearch, ClickHouse, or similar at non-trivial scale
• Have shipped a customer-facing feature backed by an LLM or similar AI API — and have opinions on where it worked and where it did not. No modelling or ML-infrastructure background required
• Frontend fluency in Vue.js — enough to ship a feature without a frontend partner
• Strong product instincts and a portfolio or GitHub of shipped work

Tech Skill Set:

Node.js (NestJS), Go, Vue.js, MongoDB, Redis, Kafka / Pub-Sub, ElasticSearch — running on a distributed microservices architecture over Istio service mesh, with AI-assisted features built on top of existing AI/LLM APIs.

What Success Looks Like:

Ships Opportunities features from idea to production with minimal cycle time
Builds services that stay reliable as workspaces grow from thousands to millions of deals
Makes sound trade-offs between speed, quality, and long-term maintainability
Ships AI-assisted features that customers actually trust — useful often enough to matter, and well-behaved when the underlying call is not
Raises the bar on data correctness, API design, and operational excellence for the product
Continuously sharpens the customer experience through iteration and instrumentation