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

Additional Information

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