AI Project Manager

Position: AI Project Manager

Location: KIR or MTV, CA (Preferred), Remote (Would be considered)

- This role is unique in its ability to not only oversee but also actively perform AI work. The TPM directly contributes to the development and implementation of models, algorithms, and infrastructure, providing hands-on support to the engineering teams.

- Guiding AI and machine learning projects from the initial concept all the way to deployment. This includes defining the project scope, setting clear milestones, and managing detailed roadmaps to keep work on track across multiple teams.

- Applying a strong computer science background to oversee the technical aspects of AI systems, from data pipelines to model training. This ensures the implementation of best practices in MLOps, CI/CD, and risk mitigation.

- Acting as the central liaison between engineers, data scientists, and business stakeholders. This service involves translating complex technical findings into clear, actionable insights for everyone involved.

- Establishing and tracking key performance indicators (KPIs) to measure a model's effectiveness. The role also focuses on enforcing best practices for model fairness and ethical development, and setting up systems to monitor performance after deployment.

Project & Planning Deliverables

- Detailed plans outlining project phases, timelines, and milestones from ideation to production.

- Detailed, actionable plans for the day-to-day execution of a project, including task assignments, resource allocation, and dependency management.

- Clearly defined project scope and functional/non-functional requirements for AI solutions.

- Documents identifying potential technical and business risks, along with strategies to mitigate them.

- Regular updates for stakeholders on project progress, key metrics, and any blockers.

Technical & Operational Deliverables

- Established and documented best practices for model versioning, continuous integration, and continuous deployment pipelines.

- Documentation outlining the technical requirements and architecture for data ingestion, processing, and feature engineering.

- Systems or reports that track and visualize key performance indicators for deployed models, such as accuracy, latency, and throughput.

- Defined procedures for monitoring model performance in a production environment and automated alerting systems for performance degradation.

Technical Contributions

- Directly produced code, model prototypes, and detailed analytical reports from hands-on work. This includes scripts for data processing, feature engineering, and debugging of complex AI issues.

- Validate model functionality, identify solutions and use cases and ensure project scaling.

Documentation & Communication Deliverables

- Simplified and concise explanations of complex AI concepts and findings for non-technical stakeholders.

- A record of key decisions made throughout the project lifecycle, including rationale and stakeholders involved.

- Official records of team meetings that track discussions, decisions, and assigned tasks to ensure accountability.

- Documentation detailing best practices for ensuring model explainability, fairness, and ethical considerations.

Qualifications:

- AI use cases experience, courses certificates

- CS background, Master+ degree

- Be responsive and Responsible/Accountable, Proactive and quick learner