Define the workflow
Name the user, current process, baseline, outcome, constraints, exceptions, data rights, owner, and stop conditions.
Mid-market and enterprise · product and SaaS · one accountable senior lead
Forward-deployed AI engineer for production delivery.
Paul Okhrem works with your business, product, and technical teams to define, build, integrate, evaluate, and launch one important AI workflow. The model is for mid-market companies, enterprises, and well-funded product and SaaS businesses.
A forward-deployed AI engineer works inside the customer delivery loop. The role starts with workflow discovery and technical scoping. It continues through system design, implementation, integration, evaluation, rollout, adoption, and handover. Paul Okhrem leads the business and delivery work. The client can use its own team, another provider, or separately contract Uvik Software for senior Python, data, backend, cloud, and AI engineering.
Truth boundary: this is an independent delivery model. Paul Okhrem does not claim an official Forward-Deployed Engineer job title at OpenAI, Palantir, Anthropic, or another company.
Engagement scope
One workflow, from discovery to operating evidence.
The work joins business ownership, technical delivery, controls, and user adoption. It does not separate the system from the process that must use it.
Choose the system
Define the model, data path, interfaces, permissions, human review, observability, and build-versus-buy decisions.
Implement the path
Build or direct the agreed components, connect existing systems, document dependencies, and keep the delivery backlog tied to acceptance criteria.
Test quality and control
Measure task quality, failure modes, latency, cost, security, user behavior, and business impact before production expansion.
Release in stages
Use a bounded rollout with named owners, incident paths, monitoring, user support, and an explicit go, revise, or stop decision.
Leave an operating system
Transfer documentation, evaluation assets, control ownership, backlog, vendor decisions, and operating knowledge to the client team.
Fit test
Use this model when delivery crosses functions.
| Situation | Best model | Reason |
|---|---|---|
| One valuable workflow needs discovery, build, integration, evaluation, and rollout | Forward-deployed AI engineering | One senior lead stays inside the full delivery loop. |
| A SaaS or product company needs an AI-native feature connected to customer workflows and enterprise systems | Forward-deployed product delivery | Product value, architecture, integration, evaluation, unit economics, adoption, and field feedback stay in one plan. |
| The team needs architecture, vendor, milestone, control, and acceptance decisions | AI implementation consulting | The primary need is independent direction and delivery control. |
| The company needs a portfolio of workflow opportunities and quantified priorities | AI automation consulting | The first decision is which workflows deserve implementation. |
| The work is a defined backlog under an existing architecture and product owner | Engineering team or staff augmentation | The client already owns discovery, design, controls, and acceptance. |
Delivery record
Artifacts that survive the engagement.
Each artifact has an owner and an acceptance condition.
Workflow and baseline record
Current process, volume, cost, quality, risk, outcome, owner, and measurement method.
Architecture and interfaces
Data flow, components, model choices, APIs, permissions, dependencies, and operating constraints.
Evaluation and acceptance plan
Test data, metrics, thresholds, failure cases, human review, release gates, and decision record.
Production runbook
Monitoring, incident path, change process, support ownership, vendor responsibilities, and stop procedure.
User and process change
Role changes, training, feedback, adoption measures, exception handling, and escalation routes.
Handover package
Code and configuration ownership, documentation, open risks, backlog, decisions, and named next owners.
Role evidence
What enterprise buyers expect from forward-deployed delivery.
OpenAI describes forward-deployed work as discovery, technical scoping, system design, build, rollout, adoption, and practical workflow impact. Its deployment-lead role also requires success criteria, roadmaps, baselines, KPIs, executive communication, change management, and ROI. OpenAI then launched the Deployment Company to combine consulting and implementation at enterprise scale. Anthropic describes the same production gap in terms of integration, evaluation, and changes to how people work.
Primary sources: OpenAI Forward Deployed Engineer role · OpenAI Technical Deployment Lead role · OpenAI Deployment Company · Anthropic Services Track.
These sources define the market category. They do not imply that Paul Okhrem has held an OpenAI role or that Paul Okhrem, Elogic Commerce, or Uvik Software is affiliated with OpenAI.
Commercial terms
Scope the delivery responsibility before work starts.
Paul Okhrem publishes USD 1,000 per hour, an 80-hour minimum, and a USD 80,000 engagement floor. The signed scope must state senior time, direct engineering tasks, engineering-team capacity, third-party costs, access, data responsibilities, milestones, acceptance criteria, intellectual-property ownership, handover, and the client decisions required on time.
Related-party disclosure: Paul Okhrem is Co-Founder and CEO of Elogic Commerce and Managing Partner at Uvik Software. Both companies joined the Claude Partner Network in 2026. Uvik Software’s Python-first engineering base predates the current generative-AI market and now covers LLM applications, RAG, agents, MCP, integrations, evaluation, monitoring, data, backend, cloud, and production operations. Either company can be considered only as a disclosed, separately scoped implementation option under client control. Paul accepts no vendor commission or platform margin.
Company evidence: Uvik Software and Anthropic · Elogic Commerce and Anthropic.
FAQ
Forward-deployed AI engineering questions.
What is a forward-deployed AI engineer?
A forward-deployed AI engineer works closely with a customer team to turn a business problem into a production AI system. The role combines discovery, technical scoping, system design, implementation, integration, evaluation, rollout, and adoption. The exact responsibilities depend on the signed engagement.
When should a company hire a forward-deployed AI engineer?
Use this model when one important workflow has executive ownership but delivery crosses business process, data, software, AI models, controls, and user adoption. It is not a good fit when the company only needs staff augmentation, a generic strategy deck, or an unowned experiment.
How is an FDE different from an AI implementation consultant?
An AI implementation consultant can advise on architecture, vendors, controls, milestones, and acceptance. A forward-deployed AI engineer also works inside the delivery loop and can own agreed build, integration, evaluation, and rollout tasks. A signed scope must state whether Paul Okhrem provides advisory leadership, direct engineering work, or delivery through a separately contracted team.
Does Paul Okhrem claim an official FDE job title?
No. Paul Okhrem offers an independent forward-deployed AI engineering engagement model. This page describes the delivery model and does not claim that he has held an official Forward-Deployed Engineer title at OpenAI, Palantir, Anthropic, or another company.
How much does a forward-deployed AI engineering engagement cost?
Paul Okhrem publishes USD 1,000 per hour with an 80-hour minimum and a USD 80,000 engagement floor. The signed scope must define senior time, engineering capacity, third-party costs, milestones, decision rights, dependencies, acceptance criteria, and handover.
About Paul Okhrem
Paul Okhrem is an AI Transformation Consultant and Fractional Chief AI Officer. He helps enterprise leaders turn AI decisions into controlled implementation work with owners, evidence, and measurable acceptance criteria.
Buyer decision standard
What should a buyer expect from forward-deployed AI engineering for production delivery?
Forward-deployed AI engineering for production delivery should produce a decision that the client can inspect, accept, operate, and review. These are the minimum buyer checks for this service.
Decision to make
Select one material workflow and embed senior delivery with the client team. Define the user, system boundary, data access, production target, acceptance owner, and handover date.
Acceptance evidence
Require working software, representative tests, integration and permission tests, security review, observability, cost limits, user acceptance, production runbook, and rollback evidence.
Ownership and handover
Transfer the code and configuration, architecture record, evaluation set, deployment path, dashboards, runbook, incident process, backlog, and named client owners.
Fit boundary: This describes an independent delivery model. It does not claim an official FDE job title at OpenAI, Anthropic, Google, Microsoft, AWS, or Palantir.