Software Development Engineer III (CRM - Opportunities - AI)

We are seeking an SDE-3 Backend Builder to own the CRM Opportunities product end-to-end. Opportunities is the deal and pipeline engine at the heart of our CRM — the system of record for every revenue motion our customers run, from lead conversion to closed-won.

This role emphasises deep backend ownership: designing data models, APIs, and distributed workflows that power pipelines, stages, automations, reporting, and integrations at scale. You will operate with high autonomy, turning ambiguous problems into production systems, with enough frontend fluency to ship the occasional UI surface without waiting on anyone.

Opportunities is also where a growing set of AI-assisted features live — things like surfacing what needs attention in a pipeline, summarising deal context, and suggesting next steps. To be clear about the shape of that work: we are not training or serving models. These features are built on top of existing AI capabilities, and the engineering problem is the product plumbing around them — assembling the right context from CRM data, keeping latency and cost sane, handling failure and non-deterministic output gracefully, and making the results trustworthy enough for a customer to act on. You do not need an ML background; you need the judgement to build a reliable product feature on top of a probabilistic dependency.

• 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

• 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

• 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

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.

• 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

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Originally posted on Himalayas