Jobs at Jeen.ai

Develop and refine prompts for AI systems, validate and QA AI-generated outputs, perform functional checks at customer sites, research AI advances, and collaborate with cross-functional teams to ensure accurate delivery.

Design and refine prompts, build automations integrating LLMs, write Python/SQL code, perform QA on AI models, research AI advances, and collaborate with cross-functional teams to deliver AI-driven products.

Manage front desk operations, office supplies and vendor relationships, support onboarding and company events, handle communications and logistics, provide administrative support to teams, and maintain office systems and service delivery.

Lead hands-on development of enterprise AI products using React, Next.js, Node.js, and NestJS; integrate generative AI; design and optimize relational and vector databases; drive architecture, testing, CI/CD, containerized deployments, and ensure performance and security.

Lead product discovery, define PRDs, prioritize and manage features from concept to launch, run customer discovery with enterprise clients, collaborate with engineering/design/sales, measure product metrics, perform acceptance testing, and iterate based on feedback and market research in the AI space.

Build and maintain end-to-end real-time voice AI systems: frontend in React+TypeScript, FastAPI Python backends, Python microservices (transcription, call analysis), integrate LLMs and STT/TTS, design telephony/SIP infrastructure, ensure scalable architecture, testing, and high code quality.

Lead technical discovery and solution design with enterprise customers, build and deliver AI/ML proofs-of-concept (NLP, conversational AI, knowledge retrieval), present to executives, respond to RFPs/RFIs, develop light production code/integrations in Python plus Java/JavaScript/TypeScript, and travel frequently (20–40%).

Lead end-to-end enterprise AI implementations, manage technical client relationships, oversee platform installations and integrations across environments, coordinate cross-functional teams, create and track project plans, and manage version updates and technical change processes.

Manual QA engineer responsible for testing web applications and services, performing REST API and end-to-end functional validations across cloud and on-premise environments, writing and executing test plans/cases, documenting bugs, and collaborating with developers and product teams to improve product quality.

Build and maintain high-performance web applications using React/Next.js and Node/NestJS, integrate generative AI, design/optimize relational and vector DB schemas, contribute to architecture, ensure code quality through testing and CI/CD.

Embed with customer teams to design and deliver AI/ML-driven solutions end-to-end: data ingestion, model integration, application logic, UI/UX, APIs, monitoring, and scaling. Collaborate on roadmaps, iterate rapidly in production, inform product/SDK direction, support pre-sales, and ensure reliability, performance, security, and compliance. Travel to customer sites (20–40%).

Provide close administrative support to the CEO and VP: manage complex calendars, coordinate communication, track tasks and initiatives, handle travel, payments, and confidential documentation, and support operational initiatives while working full-time on-site.

Lead technical pre-sales for enterprise AI: engage C-suite, design secure multi-cloud AI architectures, run workshops, build POCs, present demos, influence commercial strategy, and ensure smooth handover to delivery.

Hands-on Go-to-Market Associate supporting events, digital marketing, sales development, and GTM operations. Plan and run events, execute campaigns, track performance, support outbound outreach and CRM hygiene, and optimize workflows using HubSpot, Clay, LinkedIn Sales Navigator, Monday.com, and related tools to drive pipeline and brand visibility.

Lead product strategy and roadmap for enterprise GenAI features. Translate AI/ML concepts into requirements, design RAG and multi-agent workflows, prototype with LLM tooling, define evaluation and observability, collaborate with engineering and research, and align AI capabilities to business outcomes.