Top Artificial Intelligence Jobs

Onsite Technical Project Manager responsible for representing InfoBeans at the client site, managing offshore delivery teams, streamlining priorities, and ensuring service delivery. Requires 10+ years in software development with hands-on technology and project management experience, a Java and big data background, strong interpersonal and negotiation skills, and US work authorization. Reports to the US-based client relationship director.

Develop, maintain, and troubleshoot Java-based applications. Write clean, testable code, collaborate with team members, and participate in design and code reviews.

Perform high-volume outbound prospecting (60-80 calls/emails/day), qualify leads, schedule meetings, use Salesforce and sales automation tools for reporting, collaborate with territory and worldwide sales teams, and support lead follow-up and transition to account managers.

Own the full enterprise sales cycle for manufacturing customers: prospecting, qualifying, discovery, tailored demos, negotiation, and close. Build self-sourced pipeline, manage multi-stakeholder deals, partner with Marketing/Customer Success/Product, and ensure smooth handoffs for long-term customer success.

Responsible for full cycle recruiting across multiple roles in technology and business for Innova8 portfolio companies, while advocating for competency-based and inclusive recruiting practices.

The role involves application design and development focusing on Micro Focus Cobol and Cobol on IBM Mainframe, requiring strong SDLC knowledge.

As a Deployment Strategist, you will develop and execute deployment strategies for AI in various industries, working closely with Fortune 500 clients to map workflows and ensure product fit.

As the first marketer at Ranger, you will build the growth engine, create compelling content for developers, and manage growth channels and community relationships.

The Head of Artist Partnerships will build relationships with artists and stakeholders, drive platform adoption, and collaborate across teams to support artists effectively.

Build and evaluate machine learning and optimization models across the data science pipeline: acquire and assess data, engineer features, design algorithms, test and integrate models into products, collaborate with product and engineering, and communicate results to stakeholders.