AI Lead & Product Manager: Recruited via Arc.dev

A forward-thinking organization is seeking an AI Lead & Product Manager to build and scale AI capabilities from concept to commercialization. This is a hybrid role combining product strategy, technical leadership, and hands-on execution.

You will take ideas from zero to MVP and evolve them into production-ready, revenue-generating solutions (ranging from diagnostic agents to scalable automation tools). This role allows you to architect the company’s AI engine and manage a hybrid team of developers and data scientists.

Key Metrics for Success
Time to prototype: <10 days from idea to MVP.

Diagnostic-to-pilot conversion rate.

Recurring revenue growth from AI products.

Client ROI uplift per automation case.

Responsibilities
Roadmap & Strategy: Define and own the AI roadmap with an MVP-first approach. Co-develop pricing, packaging, and Go-To-Market (GTM) strategies with leadership.

Product Execution: Translate diagnostics and client needs into prototypes and full-scale AI implementations. Build AI diagnostic and automation tools.

Technical Management: Oversee AI solution design, development, and deployment. Manage internal and freelance AI engineers, ensuring quality and security.

Infrastructure: Set up infrastructure for AI pipelines and automation delivery. Lead data integration across CRMs, APIs, and structured/unstructured data sources.

Commercialization: Translate AI capabilities into business value through clear narratives and demos. Partner with sales and marketing on positioning.

Requirements
Experience: 5+ years in product management, technology, or AI delivery roles within startups or early-stage environments.

Track Record: Proven history of launching MVPs quickly (<10 days from idea to MVP desired) and scaling based on feedback.

Technical Fluency: Strong grasp of AI/ML fundamentals to guide engineering teams.

Leadership: Experience managing hybrid or cross-border teams.

Commercial Acumen: Ability to balance product vision with measurable business outcomes.

Nice to Have Skills
Languages/Frameworks: Python, FastAPI, LangChain.

AI/ML Platforms: OpenAI, HuggingFace, Vertex AI, Azure AI, or similar LLM stacks.

Data & Automation: Snowflake, BigQuery, dbt, Airbyte/Fivetran, Zapier/Make.

Infrastructure: GCP/AWS, Docker, GitHub Actions, CI/CD pipelines.