Product Manager

EverMind is building the next-generation AI Memory Operating System (EverMemOS) — the foundational cognitive and memory infrastructure for AI agents. We are looking for a highly driven Product Manager with strong technical depth and enterprise product experience to help define how AI products should work in the Memory Era.

Responsibilities

1. Product Strategy & Roadmap

• Own the end-to-end product strategy for EverMemOS enterprise editions, cloud services, and Memory API offerings.

• Define the product roadmap based on market trends, user research, and advancements in LLMs, agents, memory systems, RAG, and cloud-native technology.

• Transform complex AI technologies into usable, scalable enterprise solutions.

2. AI & Cloud Service Product Design

• Lead the design of core enterprise and developer-facing capabilities, including:

  • Long-term memory management

  • Enterprise data ingestion and connectors

  • Multi-agent collaboration

  • Privacy, permissioning, and governance

  • Cloud deployment and observability

• Work closely with engineering, design, and research teams to deliver high-quality releases.

3. Enterprise Customer Engagement & Scenario Building

• Understand the needs of mid-to-large enterprises across industries (tech, finance, manufacturing, professional services, etc.).

• Lead product-driven PoCs and pilots, including requirement breakdown, scenario validation, and success metrics.

• Develop repeatable enterprise patterns, solution templates, and best practices.

4. Competitive & Technology Landscape Analysis

• Monitor cutting-edge developments from OpenAI, Anthropic, Google, Meta, Databricks, and industry leaders in AI infra and memory systems.

• Analyze competitive products (Mem0, MemU, MemOS, Zep, vector databases, enterprise LLM platforms, etc.) to inform product strategy.

• Anticipate industry shifts and define how EverMemOS evolves accordingly.

5. Data-Driven Product Success

• Define and optimize product KPIs (activation, usage, retention, conversion).

• Implement instrumentation and analytics to understand user behavior and improve product performance.

• Drive ecosystem growth for developers, integrators, and plugin builders.

6. Cross-Functional Leadership

• Lead PRD creation, requirement reviews, sprint planning, and release processes.

• Communicate clearly with global engineering and business teams.

• Ensure alignment across stakeholders and drive end-to-end execution.

Requirements

1. Education

• Bachelor’s degree or above from a QS Top 50 university, majoring in Computer Science, Engineering, Mathematics, Information Systems, or related fields.

2. Professional Experience

• ~3 years of proven product management experience with successful product launches or 0→1 / 1→N execution.

• Experience with enterprise SaaS, cloud platforms, AI infrastructure, developer tools, or data products highly preferred.

• Prior experience in Silicon Valley or a global technology company is a strong plus.

3. Technical Competency (Required)

Solid understanding (or ability to rapidly learn) of:

• LLMs, agents, memory systems, RAG, embeddings, vector databases

• Cloud-native architecture (Kubernetes, microservices, API gateway, observability stack)

• Enterprise security, privacy, and compliance considerations

• Ability to translate technical concepts into product requirements and work deeply with engineering.

4. User Insight & Product Thinking

• Strong ability to understand developer and enterprise user workflows.

• Expertise in requirement decomposition, scenario modeling, and structured problem solving.

5. Strong Ownership & Self-Drive

• Comfortable with ambiguity in a fast-evolving AI landscape.

• High degree of accountability, initiative, and execution capability.

• Ability to operate in high-velocity, cross-time-zone environments.

6. Communication & Documentation

• Excellent written and verbal communication skills in English.

• Proven ability to create clear PRDs, strategy docs, and stakeholder alignments.

Nice-to-Haves

• Experience with AI agents, memory systems, RAG platforms, or model serving.

• Contributions to open-source communities (GitHub, RFCs, discussions).

• Experience with developer ecosystem building or hackathon programs.

• Familiarity with SOC2, ISO 27001, HIPAA, or other compliance frameworks.