Vericast
Senior Project Manager - AI / ML Initiatives (Remote)
Vericast, San Antonio, Texas, United States, 78208
Job Description
The Senior Project Manager in our Product Management Office (PMO) leads strategic, high-complexity projects and programs focused on artificial intelligence and machine learning initiatives. This role combines technical project leadership with business acumen, managing AI / ML implementations, data platform modernization, intelligent automation, and cross-functional digital transformation efforts. You'll orchestrate projects from concept through production deployment, ensuring AI solutions are delivered on time, within budget, and aligned with business value and ethical AI principles. As a trusted advisor to stakeholders and a mentor to project teams, you'll bridge technical, business, and consulting domains while championing agile methodologies and modern project management practices.
Key Duties / Responsibilities
AI / ML Project Leadership & Delivery: Lead end-to-end delivery of complex AI and machine learning projects, including model development initiatives, AI platform implementations, intelligent automation solutions, and generative AI integrations. Drive projects through all lifecycle phases using hybrid methodologies (Agile, Scrum, Waterfall, MLOps) tailored to AI / ML project needs. Manage project scope, timeline, budget, and quality standards while navigating the unique challenges of AI projects (model performance, data requirements, ethical considerations). Coordinate dependencies across data engineering, ML engineering, data science, and business stakeholder teams. Navigate the AI project lifecycle from use case identification through model training, validation, deployment, and monitoring. (25%)
AI & Intelligent Automation Initiatives: Support implementation and optimization of AI / ML platforms, including model training infrastructure, MLOps pipelines, feature stores, and model monitoring systems. Coordinate cross‑functional teams on projects involving generative AI applications, natural language processing, computer vision, predictive analytics, and intelligent process automation. Partner with data science, engineering, and business teams to deliver AI solutions that drive measurable business outcomes and ROI. Manage relationships with AI technology vendors, cloud providers (AWS, Azure, GCP), and AI consulting partners. Ensure responsible AI practices including bias detection, explainability, data privacy, and governance frameworks. (20%)
Stakeholder Communication & Collaboration: Serve as primary point of contact for AI project teams, business owners, and executive sponsors. Deliver clear, concise communications including status reports, executive dashboards, and risk assessments tailored for both technical and non‑technical audiences. Translate complex AI concepts and project progress into business value language for leadership. Facilitate stakeholder alignment through sprint reviews, model review sessions, steering committee meetings, and AI governance forums. Present project updates, model performance metrics, and recommendations to leadership using data visualization and storytelling techniques. (20%)
Team Coordination & Resource Management: Coordinate distributed, cross‑functional teams including data scientists, ML engineers, data engineers, software developers, UX designers, and business analysts. Manage daily stand‑ups, sprint planning, model review sessions, retrospectives, and other agile ceremonies. Monitor team velocity, sprint burndown, model development milestones, and progress against OKRs. Request and allocate specialized AI / ML resources based on skill requirements and project priorities. Navigate resource constraints in competitive AI talent markets. (15%)
Change Management & AI Adoption: Partner with business units to ensure smooth implementation of AI solutions and intelligent automation. Develop and execute change management plans addressing AI literacy, training, documentation, and user adoption. Validate that AI capabilities are adopted, monitored, and that governance controls and feedback loops are established. Review AI deliverables to ensure alignment with acceptance criteria, model performance benchmarks, and business objectives. Address organizational change resistance and AI anxiety through education and transparent communication. (10%)
Mentorship & Knowledge Sharing: Mentor junior and mid‑level project managers on methodologies, tools, and AI project best practices. Stay current on emerging trends in project management, agile practices, AI technologies, and responsible AI frameworks. Contribute to PMO process improvements and development of AI‑specific templates, frameworks, and lessons learned. Provide input to performance reviews for project team members. Build organizational AI literacy through knowledge sharing and documentation. (5%)
Risk & Change Management: Identify, assess, and mitigate AI‑specific project risks including data quality issues, model performance degradation, ethical concerns, and regulatory compliance. Evaluate scope changes and their impact on timeline, budget, resources, and model requirements. Present change requests and recommendations to leadership with supporting analysis and impact assessments. Maintain RAID logs (Risks, Assumptions, Issues, Dependencies) with AI‑specific considerations and escalate as needed. Monitor and address AI governance, security, and compliance requirements. (5%)
Qualifications Education
Bachelor's degree in Business, Computer Science, Data Science, Engineering, or related field;
Master's degree or MBA preferred
Experience 5+ years managing large‑scale, cross‑functional projects and programs in AI / ML, data science, intelligent automation, or advanced analytics domains. Proven track record leading multiple systems integration and AI implementation projects through full lifecycle. Consulting experience is strongly preferred, with demonstrated ability to quickly adapt to new business contexts, build stakeholder relationships, deliver value in client‑facing or internal consulting environments, and manage ambiguity in emerging technology spaces. Hands‑on experience with agile frameworks (Scrum, Kanban, SAFe) and traditional methodologies. Experience supporting AI / ML initiatives such as predictive modeling projects, GenAI implementations, MLOps platform buildouts, or intelligent automation programs. Background working with remote and distributed teams across technical and business functions.
Knowledge / Skills / Abilities Understanding of AI / ML concepts including supervised / unsupervised learning, model training and evaluation, feature engineering, and model deployment. Familiarity with AI / ML technology stacks, cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI), and MLOps tools. Knowledge of data pipelines, data governance, and data privacy regulations (GDPR, CCPA, AI Act) as they relate to AI projects. Awareness of responsible AI principles including fairness, transparency, explainability, and bias mitigation. Experience with project management tools (Jira, Asana, Azure DevOps, MS Project). Proficiency with collaboration platforms (Confluence, Miro, Slack, Microsoft Teams) and data visualization tools.
Project Management Excellence Advanced knowledge of PMI, Agile, and hybrid methodologies with relevant certifications (PMP, CSM, SAFe, PMI‑ACP) preferred.
Strategic Communication Ability to translate complex AI / ML concepts for business audiences and business requirements for technical teams.
Influence & Leadership Proven ability to lead without direct authority and drive consensus across diverse stakeholders including skeptics of AI technology.
Problem Solving Strong analytical skills with experience in root cause analysis, risk mitigation, and creative solution development in uncertain environments.
AI Literacy Comfort working with emerging AI technologies and ability to learn new AI concepts quickly.
Continuous Improvement Knowledge of Lean, Six Sigma, or similar methodologies a plus.
Financial Acumen Experience with budget management, forecasting, ROI analysis, and business case development for AI investments.
Consulting Mindset Structured problem‑solving approach, client service orientation, and ability to deliver actionable insights.
Exceptional Organizational Abilities Exceptional organizational abilities with strong attention to detail in fast‑paced environments. Skilled facilitator capable of running productive meetings and gaining buy‑in on innovative AI initiatives. Diplomatic approach to navigating competing priorities, organizational politics, and technical trade‑offs. Customer‑centric mindset with focus on collaboration, team building, and delivering business value. Adaptable and comfortable with ambiguity, pivoting strategies, and the iterative nature of AI development. Intellectual curiosity and enthusiasm for emerging technologies.
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Key Duties / Responsibilities
AI / ML Project Leadership & Delivery: Lead end-to-end delivery of complex AI and machine learning projects, including model development initiatives, AI platform implementations, intelligent automation solutions, and generative AI integrations. Drive projects through all lifecycle phases using hybrid methodologies (Agile, Scrum, Waterfall, MLOps) tailored to AI / ML project needs. Manage project scope, timeline, budget, and quality standards while navigating the unique challenges of AI projects (model performance, data requirements, ethical considerations). Coordinate dependencies across data engineering, ML engineering, data science, and business stakeholder teams. Navigate the AI project lifecycle from use case identification through model training, validation, deployment, and monitoring. (25%)
AI & Intelligent Automation Initiatives: Support implementation and optimization of AI / ML platforms, including model training infrastructure, MLOps pipelines, feature stores, and model monitoring systems. Coordinate cross‑functional teams on projects involving generative AI applications, natural language processing, computer vision, predictive analytics, and intelligent process automation. Partner with data science, engineering, and business teams to deliver AI solutions that drive measurable business outcomes and ROI. Manage relationships with AI technology vendors, cloud providers (AWS, Azure, GCP), and AI consulting partners. Ensure responsible AI practices including bias detection, explainability, data privacy, and governance frameworks. (20%)
Stakeholder Communication & Collaboration: Serve as primary point of contact for AI project teams, business owners, and executive sponsors. Deliver clear, concise communications including status reports, executive dashboards, and risk assessments tailored for both technical and non‑technical audiences. Translate complex AI concepts and project progress into business value language for leadership. Facilitate stakeholder alignment through sprint reviews, model review sessions, steering committee meetings, and AI governance forums. Present project updates, model performance metrics, and recommendations to leadership using data visualization and storytelling techniques. (20%)
Team Coordination & Resource Management: Coordinate distributed, cross‑functional teams including data scientists, ML engineers, data engineers, software developers, UX designers, and business analysts. Manage daily stand‑ups, sprint planning, model review sessions, retrospectives, and other agile ceremonies. Monitor team velocity, sprint burndown, model development milestones, and progress against OKRs. Request and allocate specialized AI / ML resources based on skill requirements and project priorities. Navigate resource constraints in competitive AI talent markets. (15%)
Change Management & AI Adoption: Partner with business units to ensure smooth implementation of AI solutions and intelligent automation. Develop and execute change management plans addressing AI literacy, training, documentation, and user adoption. Validate that AI capabilities are adopted, monitored, and that governance controls and feedback loops are established. Review AI deliverables to ensure alignment with acceptance criteria, model performance benchmarks, and business objectives. Address organizational change resistance and AI anxiety through education and transparent communication. (10%)
Mentorship & Knowledge Sharing: Mentor junior and mid‑level project managers on methodologies, tools, and AI project best practices. Stay current on emerging trends in project management, agile practices, AI technologies, and responsible AI frameworks. Contribute to PMO process improvements and development of AI‑specific templates, frameworks, and lessons learned. Provide input to performance reviews for project team members. Build organizational AI literacy through knowledge sharing and documentation. (5%)
Risk & Change Management: Identify, assess, and mitigate AI‑specific project risks including data quality issues, model performance degradation, ethical concerns, and regulatory compliance. Evaluate scope changes and their impact on timeline, budget, resources, and model requirements. Present change requests and recommendations to leadership with supporting analysis and impact assessments. Maintain RAID logs (Risks, Assumptions, Issues, Dependencies) with AI‑specific considerations and escalate as needed. Monitor and address AI governance, security, and compliance requirements. (5%)
Qualifications Education
Bachelor's degree in Business, Computer Science, Data Science, Engineering, or related field;
Master's degree or MBA preferred
Experience 5+ years managing large‑scale, cross‑functional projects and programs in AI / ML, data science, intelligent automation, or advanced analytics domains. Proven track record leading multiple systems integration and AI implementation projects through full lifecycle. Consulting experience is strongly preferred, with demonstrated ability to quickly adapt to new business contexts, build stakeholder relationships, deliver value in client‑facing or internal consulting environments, and manage ambiguity in emerging technology spaces. Hands‑on experience with agile frameworks (Scrum, Kanban, SAFe) and traditional methodologies. Experience supporting AI / ML initiatives such as predictive modeling projects, GenAI implementations, MLOps platform buildouts, or intelligent automation programs. Background working with remote and distributed teams across technical and business functions.
Knowledge / Skills / Abilities Understanding of AI / ML concepts including supervised / unsupervised learning, model training and evaluation, feature engineering, and model deployment. Familiarity with AI / ML technology stacks, cloud platforms (AWS SageMaker, Azure ML, GCP Vertex AI), and MLOps tools. Knowledge of data pipelines, data governance, and data privacy regulations (GDPR, CCPA, AI Act) as they relate to AI projects. Awareness of responsible AI principles including fairness, transparency, explainability, and bias mitigation. Experience with project management tools (Jira, Asana, Azure DevOps, MS Project). Proficiency with collaboration platforms (Confluence, Miro, Slack, Microsoft Teams) and data visualization tools.
Project Management Excellence Advanced knowledge of PMI, Agile, and hybrid methodologies with relevant certifications (PMP, CSM, SAFe, PMI‑ACP) preferred.
Strategic Communication Ability to translate complex AI / ML concepts for business audiences and business requirements for technical teams.
Influence & Leadership Proven ability to lead without direct authority and drive consensus across diverse stakeholders including skeptics of AI technology.
Problem Solving Strong analytical skills with experience in root cause analysis, risk mitigation, and creative solution development in uncertain environments.
AI Literacy Comfort working with emerging AI technologies and ability to learn new AI concepts quickly.
Continuous Improvement Knowledge of Lean, Six Sigma, or similar methodologies a plus.
Financial Acumen Experience with budget management, forecasting, ROI analysis, and business case development for AI investments.
Consulting Mindset Structured problem‑solving approach, client service orientation, and ability to deliver actionable insights.
Exceptional Organizational Abilities Exceptional organizational abilities with strong attention to detail in fast‑paced environments. Skilled facilitator capable of running productive meetings and gaining buy‑in on innovative AI initiatives. Diplomatic approach to navigating competing priorities, organizational politics, and technical trade‑offs. Customer‑centric mindset with focus on collaboration, team building, and delivering business value. Adaptable and comfortable with ambiguity, pivoting strategies, and the iterative nature of AI development. Intellectual curiosity and enthusiasm for emerging technologies.
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