Jobs at Simpplr

Own the technical win for complex enterprise SaaS deals from discovery through close. Lead technical discovery, solution architecture, tailored demos, proof-of-concept builds, AI agent integrations, and RFP/RFI responses. Architect integrations using APIs and MCP, explain AI concepts and business value to executive audiences, support competitive positioning, and relay customer feedback to Product and Engineering. Represent the company through webinars, conferences, and technical content.

Writes and maintains customer-facing technical documentation, including knowledge-base articles, admin guides, troubleshooting content, release notes, API documentation, in-app help, and UX microcopy. Partners with Support, Product, Design, and Engineering to document features, improve self-service, support AI assistants, maintain content quality, and manage documentation workflows. Creates screenshots and recordings, analyzes support trends, edits content, and owns documentation for designated product areas.

Manage SaaS implementation projects for commercial and enterprise customers. Responsibilities include defining scope, developing project plans, managing portfolios, schedules, budgets, risks, issues, KPIs, work effort, reporting, and customer expectations. The role collaborates with implementation and cross-functional teams, supports complex implementations using waterfall and agile methodologies, and assists less experienced project managers.

Lead the architecture and production implementation of real-time multilingual Voice AI systems. Build streaming ASR, TTS, voice pipelines, conversational agents, enterprise integrations, workflow execution, security, compliance, human handoff, evaluation, and observability. Optimize performance across noisy environments, accents, languages, PSTN, and mobile audio. Establish platform standards, production readiness, and technical direction while mentoring engineers and leading design reviews.

Own the enterprise search product for Simpplr and Magnus, defining roadmap and metrics for ingestion, indexing, vector and hybrid retrieval, ranking, and RAG grounding. Partner with engineering, ML, and cross-functional teams to improve relevance, latency, security, and adoption, and establish evaluation frameworks, monitoring, and permission-aware retrieval.