AI/ML Engineer (Contract)

*Note: this is contract position through the end of 2026, with possibility of extension. Candidates based in San Diego are preferred. Remote candidates will also be considered based on qualifications.*

Job Summary:

As an AI/ML Engineer, you will design, develop, and deploy AI systems and machine learning models to automate processes and solve business problems. You will provide strategic, analytical, and technical expertise to solve critical business problems based on data, and will help collect, clarify, and translate business requirements into analytical use cases. You will also be responsible for creating models including data collection and analysis, defining information requirements, maintenance and enhancements, to help drive key business decisions.

Essential Duties and responsibilities:

• Design, build, and deploy end-to-end AI/ML and agent-based systems, from problem definition and model development to production deployment, monitoring, and continuous improvement to solve business problems, and automate enterprise and scientific tasks.
• Focus on simulating human learning activities, improving system performance through data analysis, and developing deep learning frameworks and systems.
• Collaborate with scientists to build robust data pipelines and ensure high-quality training data.
• Design scalable, reliable services on major cloud platforms; strong CI/CD, observability, and operational excellence.
• Translate customer requirements to business solutions using data pipelines and statical models.
• Build & maintain scalable ML infrastructure, including training pipelines, feature stores, and model serving systems.
• Contribute to MLOps best practices, including CI/CD for ML, model versioning, and A/B testing frameworks.
• Create exploratory analysis, model design & training, validation, feature engineering, production handoff to drive business optimization.
• Responsible for constructing, studying, and training algorithms that learn from complex, high-dimensional data to uncover patterns and develop practical predictive models and applications.
• Document architectures, experiments, and results clearly for technical and non-technical stakeholders to support current work and any retraining for the future.
• Data, model, and agent pipeline engineering (e.g., workflow orchestration, model lifecycle management, automated retraining/rollouts).
• Orchestration and integration across components (agent frameworks, containers, web services/APIs, distributed systems).
• Develop and integrate intuitive Copilot experiences into existing tools to provide real-time, AI-driven assistance and insights to team members.