Use Case & Outcome Definition
Translate the business problem into objectives, users, constraints, success metrics, and deliverables.
Freelance AI project management
Turn AI ambition into shippable, measurable work
I help teams coordinate AI products and AI-enabled workflows from business problem and requirements through delivery, testing, launch, adoption, operations, and continuous improvement.
255%Qualified-lead increase
AI SaaSHands-on operating experience
50+Completed projects
Top Rated PlusUpwork status
What I manage
AI delivery without losing sight of the business
I do not position myself as the machine-learning engineer. My role is to make sure the right problem is being solved, the work is coordinated, quality is evaluated, stakeholders stay aligned, and the finished system creates usable business value.
Requirements & Backlog
Structure requirements, priorities, acceptance criteria, dependencies, decisions, and delivery sequencing.
AI Product Coordination
Coordinate product, engineering, operations, subject-matter experts, clients, and leadership.
AI Workflow Automation
Map current processes, identify automation opportunities, and manage implementation into real operations.
Evaluation & Testing
Organize review criteria, test scenarios, human feedback, defects, edge cases, and acceptance evidence.
Risk & Scope Control
Keep assumptions, limitations, dependencies, changes, operational risks, and unresolved decisions visible.
Launch & Adoption
Coordinate readiness, documentation, training, rollout, stakeholder communication, and post-launch stabilization.
Performance & Iteration
Connect user feedback and operational metrics back into prioritization and continuous improvement.
AI SaaS experience
From AI output to an operating system around it
255%
increase in qualified leads
AI outreach platform
Scaling quality, operations, and campaign execution
I joined an AI SaaS engagement initially focused on reviewing and improving AI-generated messaging. The role expanded into team and customer-success operations, campaign coordination, quality control, KPI tracking, reporting, and process design.
QualityStructured review of AI-generated output and feedback.
PeopleExpanded from individual contributor into team management.
OperationsBuilt clearer campaign schedules, ownership, and reporting.
OutcomeOperational improvements supported a 255% increase in qualified leads.
AI delivery framework
A disciplined path from “we should use AI” to production value
Define the job
Clarify the user problem, desired outcome, workflow, constraints, baseline, and measurable success.
Design the delivery plan
Break the initiative into requirements, milestones, owners, dependencies, evaluation points, and decisions.
Coordinate build & integration
Keep technical work aligned with product, operational, customer, and implementation requirements.
Evaluate real outputs
Organize testing around representative scenarios, quality criteria, edge cases, and human review.
Launch into the workflow
Prepare users, documentation, controls, support, communications, and operational ownership.
Measure & improve
Track business outcomes and user feedback, then convert learning into the next delivery priorities.
Why AI projects need PM discipline
The technology can be probabilistic. The project cannot be.
Quality is not binary
AI output often requires evaluation criteria and human judgment rather than a simple pass/fail test.
Iteration is expected
The plan needs room for learning without turning every new discovery into uncontrolled scope.
Workflow matters
A technically impressive system has little value if it does not fit how users actually perform the work.
Business metrics matter more
Model behavior should ultimately connect to adoption, efficiency, revenue, quality, or another defined outcome.
Client feedback
Trusted in an AI-first operating environment
“We initially hired Eli as a message editor for our AI. Eli did a terrific job and is now managing a full team of editors. We would love to move with Eli toward a CSM management role.”
“His impeccable organizational skills were evident in the seamless management of our campaign schedule. The campaigns rolled out like clockwork and potential hurdles were proactively addressed.”
“Eli is an extremely talented individual. He went from zero knowledge to being the knowledge specialist at our company. I highly recommend Eli for his strong work ethic, honesty, and affable nature.”
Frequently asked questions
Before we work together
Are you an AI or machine-learning engineer?
No. I work as the project and operations leader around AI delivery, coordinating technical specialists and business stakeholders rather than presenting myself as the engineer building the underlying models.
Can you manage an AI SaaS product or feature?
Yes. I can manage requirements, backlog, stakeholders, delivery, evaluation, operational readiness, launch, reporting, and iteration around AI-enabled products and features.
Can you manage AI workflow automation projects?
Yes. I can map the current process, define the target workflow, coordinate technical implementation, manage testing, document the new operating model, and support adoption.
Can you coordinate developers and non-technical stakeholders?
Yes. Translating between technical delivery and business requirements is a core part of my project-management work.
Can we hire you through Upwork?
Yes. Upwork is the preferred contracting route. My profile is Top Rated Plus with a 100% Job Success Score.
Move the AI project forward
Give the initiative an owner for everything between idea and outcome
Share what you are building, the business problem, the teams involved, and where delivery is getting stuck. I’ll help create the structure needed to move it forward.
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