Use Case & Outcome Definition
Translate the business problem into clear objectives, users, constraints, success metrics, priorities, and measurable deliverables.
Independent AI project management
I help teams turn AI products and AI-enabled workflows into structured delivery plans, coordinating requirements, technical dependencies, testing, launch, adoption, operations, and continuous improvement.
What I manage
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.
Translate the business problem into clear objectives, users, constraints, success metrics, priorities, and measurable deliverables.
Structure requirements, priorities, acceptance criteria, dependencies, decisions, and delivery sequencing into an actionable plan.
Coordinate product, engineering, operations, subject-matter experts, clients, and leadership around shared AI delivery outcomes.
Map existing processes, identify practical automation opportunities, and coordinate implementation into real business operations.
Organize evaluation criteria, test scenarios, human feedback, defects, edge cases, quality reviews, and acceptance evidence.
Keep assumptions, limitations, dependencies, changes, operational risks, and unresolved decisions visible throughout delivery.
Coordinate readiness, documentation, training, rollout, stakeholder communication, adoption, and post-launch stabilization.
Connect user feedback, AI performance, and operational metrics back into prioritization and continuous improvement.
AI SaaS experience
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.
AI delivery framework
Clarify the user problem, desired business outcome, workflow, constraints, baseline, and measurable success criteria.
Break the initiative into requirements, milestones, owners, dependencies, evaluation points, and key decisions.
Keep technical work aligned with product, operational, customer, and implementation requirements throughout delivery.
Organize testing around representative scenarios, quality criteria, edge cases, human review, and documented acceptance evidence.
Prepare users, documentation, controls, support, communications, and operational ownership for successful adoption.
Track business outcomes and user feedback, then turn what is learned into the next delivery priorities and improvement cycles.
Why AI projects need PM discipline
AI output often requires defined evaluation criteria, representative scenarios, and human judgment rather than a simple pass or fail test.
AI initiatives need room for learning and refinement without turning every new discovery into uncontrolled scope expansion.
A technically impressive AI system creates limited value if it does not fit how users actually perform the work.
AI performance should ultimately connect to adoption, efficiency, revenue, quality, or another clearly defined business outcome.
Client feedback
“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
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.
Yes. I can manage requirements, backlog, stakeholders, delivery, evaluation, operational readiness, launch, reporting, and iteration around AI-enabled products and features.
Yes. I can map the current process, define the target workflow, coordinate technical implementation, manage testing, document the new operating model, and support adoption.
Yes. Translating between technical delivery and business requirements is a core part of my project management work.
Yes. I can coordinate evaluation criteria, representative test scenarios, human review, issue tracking, acceptance evidence, and feedback loops without presenting myself as the underlying ML engineer.
Yes. Upwork is the preferred contracting route. My profile is Top Rated Plus with a 100% Job Success Score.
Move the AI project forward
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.
Available for 1099 contract and Upwork engagements only — not eligible for W-2 employment in the U.S.
Thank you. I’ll respond as soon as possible.