AI/ML Technical Delivery Manager
Think Big Solutions, Inc
Behavioral Health Market Context
Apply Nowvia Dice
Job Description
es, technical dependencies, delivery risks, and cross-functional execution.
Unlike strategy-led roles, this position is execution-centric — focused on ensuring AI programs are delivered on time, within scope, and aligned to measurable business outcomes.
Key Responsibilities
1. End-to-End Delivery Management
Own execution of AI/ML projects from initiation through deployment. Define delivery plans, milestones, and implementation timelines. Ensure alignment between business objectives and technical execution. Manage interdependencies across multiple AI initiatives
2. AI Oversight.
Coordinate technical execution across various teams like Data engineering (Data Readiness), Infrastructure, Security(Platform readiness) and AI/ML team (Use Case , Model, AI application tool readiness.)
3. Ensure transition from:
Proof-of-Concept → Pilot → Production
4. Cross-Functional Execution
Work closely with: Function Delivery Focus AI/Data Science Model development timelines Data Engineering Data pipelines ML Engineering Model deployment Cloud Teams Infrastructure readiness Product Teams Business integration Governance Teams Compliance Security Teams Security
5. Risk & Dependency Management
Identify delivery risks specific to AI initiatives: Data quality issues, Model instability, Infrastructure limitations, mitigate delays caused by experimentation cycles
6. Stakeholder Engagement
Provide transparent delivery reporting, translate technical progress into business outcomes, Align stakeholders across business and technical domains.
7. Governance & Compliance Support
Ensure adherence to AI governance standards, Support model validation and documentation, Assist in responsible AI implementation
8. Value – ROI Realization
Track deployment success metrics, Support adoption of AI solutions, Ensure delivery aligns with expected ROI
Required Qualifications
Education
• Bachelor’s or master’s degree in:
• Computer Science, Engineering, Data Science, Information Systems, Business Technology
Experience
• 7+ years in technology delivery or project management.
• Experience delivering AI / ML solutions in enterprise environments.
• Experience managing cross-functional technical teams.
• Technical Understanding of Machine learning lifecycle, Data engineering workflows, Model deployment processes, AI solution architecture basics with a basic understanding of GenAI, LLMs, RAG, Agentic AI etc.
• Basic knowledge with cloud computing platforms ( Google Cloud, Azure) for model deployment and scaling
Preferred Skills
Agile / Scrum delivery experience, Familiarity with MLOps practices, Knowledge of AI governance frameworks, Experience with enterprise cloud AI platforms
Core Competencies
• Competency Description
• Execution Leadership Deliver AI solutions
• Technical Coordination Align ML workflows
• Risk Management Manage delivery complexity
• Communication Bridge business & tech
• Stakeholder Alignment Ensure adoption
• Success Metrics
• On-time AI deployment, Production model stability, Adoption rate of delivered AI solutions, Reduction in delivery risk, Business value achieved
Unlike strategy-led roles, this position is execution-centric — focused on ensuring AI programs are delivered on time, within scope, and aligned to measurable business outcomes.
Key Responsibilities
1. End-to-End Delivery Management
Own execution of AI/ML projects from initiation through deployment. Define delivery plans, milestones, and implementation timelines. Ensure alignment between business objectives and technical execution. Manage interdependencies across multiple AI initiatives
2. AI Oversight.
Coordinate technical execution across various teams like Data engineering (Data Readiness), Infrastructure, Security(Platform readiness) and AI/ML team (Use Case , Model, AI application tool readiness.)
3. Ensure transition from:
Proof-of-Concept → Pilot → Production
4. Cross-Functional Execution
Work closely with: Function Delivery Focus AI/Data Science Model development timelines Data Engineering Data pipelines ML Engineering Model deployment Cloud Teams Infrastructure readiness Product Teams Business integration Governance Teams Compliance Security Teams Security
5. Risk & Dependency Management
Identify delivery risks specific to AI initiatives: Data quality issues, Model instability, Infrastructure limitations, mitigate delays caused by experimentation cycles
6. Stakeholder Engagement
Provide transparent delivery reporting, translate technical progress into business outcomes, Align stakeholders across business and technical domains.
7. Governance & Compliance Support
Ensure adherence to AI governance standards, Support model validation and documentation, Assist in responsible AI implementation
8. Value – ROI Realization
Track deployment success metrics, Support adoption of AI solutions, Ensure delivery aligns with expected ROI
Required Qualifications
Education
• Bachelor’s or master’s degree in:
• Computer Science, Engineering, Data Science, Information Systems, Business Technology
Experience
• 7+ years in technology delivery or project management.
• Experience delivering AI / ML solutions in enterprise environments.
• Experience managing cross-functional technical teams.
• Technical Understanding of Machine learning lifecycle, Data engineering workflows, Model deployment processes, AI solution architecture basics with a basic understanding of GenAI, LLMs, RAG, Agentic AI etc.
• Basic knowledge with cloud computing platforms ( Google Cloud, Azure) for model deployment and scaling
Preferred Skills
Agile / Scrum delivery experience, Familiarity with MLOps practices, Knowledge of AI governance frameworks, Experience with enterprise cloud AI platforms
Core Competencies
• Competency Description
• Execution Leadership Deliver AI solutions
• Technical Coordination Align ML workflows
• Risk Management Manage delivery complexity
• Communication Bridge business & tech
• Stakeholder Alignment Ensure adoption
• Success Metrics
• On-time AI deployment, Production model stability, Adoption rate of delivered AI solutions, Reduction in delivery risk, Business value achieved
Qualifications
- •Bachelor’s or master’s degree in:
- •Computer Science, Engineering, Data Science, Information Systems, Business Technology
- •7+ years in technology delivery or project management
- •Experience delivering AI / ML solutions in enterprise environments
- •Experience managing cross-functional technical teams
- •Technical Understanding of Machine learning lifecycle, Data engineering workflows, Model deployment processes, AI solution architecture basics with a basic understanding of GenAI, LLMs, RAG, Agentic AI etc
- •Basic knowledge with cloud computing platforms ( Google Cloud, Azure) for model deployment and scaling
- •Agile / Scrum delivery experience, Familiarity with MLOps practices, Knowledge of AI governance frameworks, Experience with enterprise cloud AI platforms
- •Execution Leadership Deliver AI solutions
- •Risk Management Manage delivery complexity
- •Communication Bridge business & tech
- •Success Metrics
- •On-time AI deployment, Production model stability, Adoption rate of delivered AI solutions, Reduction in delivery risk, Business value achieved
Benefits
Responsibilities
- •The Project Delivery Manager – AI/ML is accountable for the successful execution and operational delivery of Artificial Intelligence and Machine Learning initiatives
- •This role ensures AI solutions move beyond experimentation into scalable, production-grade deployments by managing timelines, technical dependencies, delivery risks, and cross-functional execution
- •Unlike strategy-led roles, this position is execution-centric — focused on ensuring AI programs are delivered on time, within scope, and aligned to measurable business outcomes
- •End-to-End Delivery Management
- •Own execution of AI/ML projects from initiation through deployment
- •Define delivery plans, milestones, and implementation timelines
- •Ensure alignment between business objectives and technical execution
- •Manage interdependencies across multiple AI initiatives
- •AI Oversight
- •Coordinate technical execution across various teams like Data engineering (Data Readiness), Infrastructure, Security(Platform readiness) and AI/ML team (Use Case , Model, AI application tool readiness.)
- •Proof-of-Concept → Pilot → Production
- •Cross-Functional Execution
- •Work closely with: Function Delivery Focus AI/Data Science Model development timelines Data Engineering Data pipelines ML Engineering Model deployment Cloud Teams Infrastructure readiness Product Teams Business integration Governance Teams Compliance Security Teams Security
- •Risk & Dependency Management
- •Identify delivery risks specific to AI initiatives: Data quality issues, Model instability, Infrastructure limitations, mitigate delays caused by experimentation cycles
- •Stakeholder Engagement
- •Provide transparent delivery reporting, translate technical progress into business outcomes, Align stakeholders across business and technical domains
- •Governance & Compliance Support
- •Ensure adherence to AI governance standards, Support model validation and documentation, Assist in responsible AI implementation
- •Value – ROI Realization
- •Track deployment success metrics, Support adoption of AI solutions, Ensure delivery aligns with expected ROI
- •Technical Coordination Align ML workflows
- •Stakeholder Alignment Ensure adoption
More Jobs
- Behavioral Technician / RBT – Entry Levelat Behavioral Innovations
- School RBT Jobat Sunbelt Staffing
- BCBA: Lead Therapy, Mentorship & Growth Pathat Action Behavior Centers
- ABA Behavior Technician — 1:1 Client Impact & Trainingat Magin Behavioral Therapies
- ABA Behavioral Technician: RBT Training & Flexible Scheduleat Therapeutic Learning Consultants
- Behavior Technicianat Centria Autism
- BCBA Center and Home Basedat Yellow Bus ABA
- Clinical Research Associate - Orthopaedicsat CEDARS-SINAI
- Director of Clinical Research and Real World Evidenceat Virta Health
- Clinical Trials Coverage Analystat Virtual Vocations Inc