AI Operations Manager

About us

Solvedex is a fast-growing Talent-as-a-Service (TaaS) startup focused on delivering high-quality, flexible talent solutions. We specialize in a fractional talent model that enables organizations to access top-tier professionals without the constraints of traditional hiring.

About the role

The AI Operations Manager will lead the design, deployment, and maintenance of productionized AI solutions. You'll bridge the data science and engineering worlds, owning CI/CD pipelines, model lifecycle management, and cross-functional delivery.

What You'll Do

• Own the end-to-end delivery of productionized ML/DL/LLM solutions, from design and development to deployment and ongoing performance. 

• Partner with Data Science teams on solution design and delivery; ensure ongoing monitoring, maintenance, and performance of AI solutions in production.

• Champion compliance, security, and governance best practices across all AI/ML deployments.

• Coordinate with Release Management, Infrastructure, and DevOps to ensure reliable, smooth deployments.

Minimum Qualifications

• Bachelor's degree in Statistics, Mathematics, Engineering, Data Science, Computer Science, Economics, or equivalent experience.

• 5+ years of experience in AI Ops, ML Engineering, Data Science, Data Engineering, DevOps, or related fields.

• Advanced English proficiency (written and spoken). You'll work directly with a U.S.-based client and cross-functional teams.

Preferred Qualifications

• Master's degree in a technical field (Data Science, Computer Science, Engineering, etc.).

• 8+ years of experience across AI/ML operations or related disciplines.

• Hands-on experience building or supporting enterprise-scale generative AI solutions, including LLM-based applications in high-volume production environments.

• Background in regulated industries (financial services a strong plus), with solid understanding of data privacy, AI governance, and compliance frameworks.

• Track record of maturing AI/ML operational processes and driving efficiency across onshore/offshore teams.

Technical Skills

• Python

• AWS (cloud infrastructure for ML workloads)

• Databricks

• CI/CD pipelines & MLOps tooling (MLflow, Kubeflow, or similar)

• DevSecOps principles

• Machine Learning & Deep Learning techniques

• LLMs / Generative AI in production