Technical Deployment, Applied AI

Job highlights Identified by Google from the original job post 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 • 10 more items(s) 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 • 20 more items(s) More job highlights Job description Title: AI/ML Technical Delivery Manager

Location: Carrollton / Atlanta, GA (Onsite / Hybrid)

Duration: Long Term

Role Summary

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 b...y 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.

Key Responsibilities

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.)

Ensure transition from:

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

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 Show full description Report this listing Loading...