You will take end-to-end ownership of machine learning models, ensuring their reliable deployment and performance on physical hardware. Additionally, you will design and optimize real-time AI pipelines while collaborating with cross-functional teams and clients.
Marvik
11 Remote Job Openings at Marvik
The AI Business Manager will bridge the gap between business and engineering teams to design AI solutions and manage the full project lifecycle from discovery to kickoff. Responsibilities include building commercial proposals, coordinating with technical teams, and maintaining CRM accuracy.
Design, implement, and maintain automated test suites for web, mobile, and API applications while integrating them into CI/CD pipelines. Collaborate with development and product teams to define testing strategies and ensure high product quality through bug tracking and code reviews.
Partner with customers to translate complex business challenges into scalable, production-ready AI solutions. Design and architect end-to-end AI systems while collaborating with cross-functional teams to deliver measurable business impact.
Lead discovery sessions with stakeholders to map complex business processes and define end-to-end decision logic for AI-driven solutions. Drive validation and testing efforts while maintaining clear documentation of workflows, business rules, and system interactions.
Manage and grow strategic relationships with hyperscalers like Microsoft, AWS, Google Cloud, and Oracle across the US and LATAM. Drive partner program strategy, activate co-sell opportunities, and execute joint go-to-market initiatives to generate pipeline.
Lead the architectural direction and development of production-grade agentic AI workflows and SaaS products across Azure and AWS. Mentor engineers and establish organization-wide technical standards and engineering practices.
Lead the marketing and go-to-market function to position Marvik within the enterprise AI market and drive pipeline growth. Responsibilities include defining GTM strategy, managing demand generation, aligning with sales, and building a high-performing marketing team.
Lead the Intelligence strategy and roadmap while building and managing a high-performing AI/ML and data engineering team. Establish the canonical data substrate and AI platform, focusing on RAG architectures, MLOps, and evaluation frameworks.
Build and operate scalable ingestion and transformation pipelines to turn raw fabrication data into AI-ready datasets. Implement medallion-style architectures, vector pipelines for RAG, and real-time CDC flows from MongoDB.
Lead the architectural direction and development of production-grade agentic AI workflows and SaaS products. Mentor engineers and establish technical standards across the organization to ensure high performance and quality.