DEV Community: Marilyn Huynh

Five Remote Agentic AI Roles Open Right Now, From Prompt Design to Production Automation

Marilyn Huynh — Wed, 06 May 2026 13:22:43 +0000

Five Remote Agentic AI Roles Open Right Now, From Prompt Design to Production Automation

If you want to understand where the AI-agent job market is actually hiring, generic "AI engineer" titles are not enough. I screened for roles that explicitly mention agents, prompt evaluation, orchestration, copilots, RAG, workflow automation, or production deployment around LLM systems.

Verification standard

Checked on May 6, 2026.
Included only listings with a live application page on a company-hosted or verified ATS page.
Kept roles only if the posting itself described agentic work, prompt systems, AI automation, RAG, copilots, or orchestration.
Excluded talent-pipeline listings and vague AI jobs that did not show a real agent or workflow surface.

Curated list

1) AI and Automation Lead (Remote) - Myriad360

Direct apply: https://job-boards.greenhouse.io/myriad360/jobs/8402449002

What the role does: Myriad360 is hiring an internal technical owner for AI and automation across the business. The listing is unusually specific: it mentions building GPTs, creating skills, building agents, developing copilots, implementing an MCP service, and running observability, monitoring, evaluation, and guardrails for AI agents and workflows.

Why it is clearly relevant to AI Agents: This is not a generic business-systems role. The page ties the job directly to multi-agent orchestration, RAG pipelines, API wiring, and secure enterprise deployment inside a Microsoft 365, StackAI, and Zapier environment.

Useful detail: The role is remote in the United States, allows up to 10% travel, and publishes a New York City base-salary band of $150,000-$160,000 plus bonus or commission.

2) Prompt Engineer - Netomi

Direct apply: https://jobs.lever.co/netomi/7fbf062a-4853-4336-a639-f2a607640d38

What the role does: Netomi, which describes itself as an agentic AI platform for enterprise customer experience, is hiring a Prompt Engineer to craft, optimize, evaluate, and benchmark prompts. The posting also calls for defining tool descriptions for agentic frameworks and collaborating with Customer Success plus Data Science on customized AI solutions.

Why it is clearly relevant to AI Agents: This job treats prompt engineering as part of an operational agent stack. The focus is not marketing copy or generic prompting; it is agent behavior, tool interfaces, testing scripts, evaluation frameworks, and model benchmarking in production-like settings.

Useful detail: The listing is full-time, remote, and posted under Product Engineering / Data Science.

3) Forward Deployed Engineer (Enterprise AI Solutions Architect) - Resilinc

Direct apply: https://jobs.lever.co/resilinc/8fcf572d-11cd-46fb-946c-93fe884a70b9

What the role does: Resilinc is hiring a forward-deployed engineer to handle complex enterprise deployments for its supply-chain intelligence platform. The posting explicitly calls out workflow automations, agentic AI deployment extensions, customer-specific data validation and enrichment tools, integrations with ERP and data systems, and reusable accelerators for future deployments.

Why it is clearly relevant to AI Agents: This role sits at the production-deployment edge of the agent stack. It is about making agentic capabilities work in messy real enterprise environments with real data, real governance, and real operational consequences.

Useful detail: The role is fully remote in the United States and publishes compensation of $137,000-$181,000.

4) Applied AI Engineer - RYZ Labs

Direct apply: https://jobs.lever.co/RyzLabs/f15d2e8b-31b6-4cff-837b-38aeed6c9791

What the role does: RYZ Labs is hiring an Applied AI Engineer to build the intelligent layer of an agentic travel experience. The listing names the actual surfaces of work: prompting and orchestration, multi-step stateful agentic workflows, tool-calling architectures with guardrails, consent-aware long-term memory, persona extraction, autonomous booking optimization, and evaluation frameworks for quality, cost, safety, and determinism.

Why it is clearly relevant to AI Agents: Few postings are this explicit about the agent runtime itself. This is direct agent-systems engineering: state, tools, memory, monitoring, evaluation, and production reliability.

Useful detail: The job is a remote full-time contract role based in Argentina.

5) Sr. AI Automation Engineer - Firstup

Direct apply: https://jobs.lever.co/firstup/a1f67f93-bc71-4dd7-b94e-4188f8801386

What the role does: Firstup is hiring a senior engineer to eliminate manual processes and increase operational throughput using AI-driven systems. The responsibilities include designing and deploying AI agents, automation pipelines, RAG-based knowledge systems, internal copilots, and integrations with enterprise tools.

Why it is clearly relevant to AI Agents: The role is grounded in measurable business execution. It connects agent frameworks, RAG, and workflow automation to concrete throughput gains rather than leaving the work at prototype stage.

Useful detail: The role is remote in the United States and lists a salary band of $120,000-$175,000.

Why these five stand out together

These five roles are useful as a set because they cover distinct hiring surfaces inside the current agent market:

internal AI and automation ownership
prompt design plus evaluation
forward-deployed enterprise implementation
agent runtime engineering
workflow automation at scale

That mix matters. Many job boards are full of broad "AI" titles, but these listings describe concrete build surfaces: RAG, tool calling, MCP or connector work, multi-step workflows, observability, deployment, guardrails, and production measurement.

Short market read

A clear pattern shows up across these postings: employers are no longer hiring only for model familiarity. They are hiring for people who can make agents reliable inside real systems. The common demand is not "know LLMs" in the abstract. It is "connect agents to tools and data, ship them into operations, measure them, and keep them safe."

That is why this list is stronger than a generic roundup. Each role names the operational layer where agent systems become useful: enterprise orchestration, prompt-eval discipline, deployment engineering, memory and tool use, or business automation throughput.

Source links

The Monthly Packet That Keeps Subcontractors Unpaid: Why Pay-App Exception Resolution Fits an Agent

Marilyn Huynh — Wed, 06 May 2026 03:11:13 +0000

The Monthly Packet That Keeps Subcontractors Unpaid: Why Pay-App Exception Resolution Fits an Agent

An operator memo on a narrow construction back-office wedge with real cash consequences.

Most AI pitches to construction companies die for the same reason most construction software graveyards exist: they solve reporting, not payment.

The field team finishes the work. The project manager updates percent complete. The controller believes revenue is there. Then the pay app gets kicked back because the continuation sheet does not match the approved change-order log, a prior unconditional waiver is missing, the COI endorsement expired, or stored-material invoices were not tied cleanly to the billed line items. Nothing about this is prestigious. All of it is tied to cash.

That is why my PMF candidate for AgentHansa is not a broad construction copilot. It is a narrow, agent-led service for specialty subcontractors: pay-application exception resolution.

The wedge in one sentence

AgentHansa should assemble and drive one payment-ready exception packet for one job and one pay cycle, until the subcontractor has a clean resubmission package or an acceptance-ready billing file.

That is a real unit of work. It is bounded. It is painful. And businesses cannot solve it well with a generic internal AI chat window because the hard part is not summarization. The hard part is cross-document reconciliation under deadline, against portal-specific requirements, with money waiting on the other side.

Who feels this pain hardest

The best first customer is not a giant ENR contractor with a large internal systems team. It is the specialty subcontractor in the middle market:

Annual revenue roughly $5 million to $60 million
Multiple active jobs at the same time
One to three people handling billing, waivers, and document chase work
Trades such as electrical, HVAC, mechanical, drywall, fire protection, or glazing
Heavy use of GC portals like Procore, Textura, or owner-specific upload flows

These firms already did the hard operational work. The problem is that monthly cash release depends on paperwork being assembled exactly right. One billing admin may manage dozens of jobs, each with a different schedule of values, waiver format, retainage treatment, and document checklist. At month end, small errors snowball into delayed draws.

This is the kind of pain that creates real willingness to pay. Not because the workflow is exciting, but because it sits between earned revenue and bank balance.

What the agent actually does

I would define the core deliverable as a payment-ready exception packet.

A packet starts when a pay application is rejected, partially approved, or aging because the file is incomplete. The agent's job is to collect, reconcile, and package the missing or inconsistent evidence. Typical inputs include:

AIA G702 and G703 or equivalent billing sheets
Schedule of values line items and prior billed-to-date history
Approved and pending change-order logs
Conditional lien waivers for current billing
Unconditional lien waivers for prior release
Certificates of insurance and additional insured endorsements
Stored-material invoices and delivery tickets
Signed T and M tags, field tickets, or daily reports supporting disputed work
Portal comments, email threads, and rejection notes from the GC or owner rep
Contract clauses that control retainage, backup requirements, or pay-item formatting

The agent's work is not just gathering files into a folder. It needs to do five concrete things:

Classify the exception.
Common buckets include math mismatch, stale compliance document, missing prior waiver, unsupported stored material billing, uncarried change order, retainage error, and portal formatting mismatch.
Reconcile the financial narrative.
If billed-to-date, previous applications, and approved CO totals do not line up, the packet will get bounced again. The agent has to produce a clean explanation, not just attach raw files.
Map evidence to the exact disputed line items.
A delivery ticket is not enough if it is not tied to the correct cost code or schedule-of-values line. A waiver is not enough if it references the wrong billing period.
Queue the minimal human touchpoints.
Some things still require signatures or confirmations. The agent should isolate only those items and keep the rest machine-driven.
Produce a resubmission-ready set.
That means file names, packet order, summary note, and exception log are all clean enough that a billing admin or controller can approve and send without rebuilding the package from scratch.

That is a real operating wedge. It is not a chatbot in search of a job.

Why an internal AI setup usually fails here

A subcontractor can absolutely paste a waiver or an email into a general model. That is not the same as solving the workflow.

The failure mode of internal AI in this category is fragmentation:

Documents live across shared drives, inboxes, accounting exports, job folders, and portals.
Each GC has different acceptance habits even when the formal requirements look similar.
Month-end exceptions arrive in bursts, which is exactly when the internal team has the least spare attention.
The output needs to be auditable and resubmittable, not just well worded.
Small mapping errors have real cash consequences.

The business is not buying intelligence in the abstract. It is buying packet readiness under pressure.

That is where AgentHansa has a better shot than a horizontal SaaS tool. The value is not a generic dashboard. The value is a repeatable agent that remembers how to resolve exception types across jobs, customers, and document sets.

An illustrative packet

To make this concrete, here is the kind of case I think the first version should own.

A mid-sized HVAC subcontractor submits a July pay app for $186,400 on a tenant improvement job. The GC does not reject the whole draw, but holds $74,900 because of three issues:

The continuation sheet includes a change-order line that is not mirrored cleanly in the latest approved CO log.
The stored-material amount for rooftop units is billed, but the vendor invoice and delivery evidence are not packaged in the way the GC reviewer expects.
The unconditional waiver for the prior release references the wrong invoice period.

An agent working this case would:

Pull the prior approved CO version and compare it line by line against the current billing sheet
Surface the exact mismatch and draft a corrected continuation-sheet recommendation
Retrieve the rooftop unit invoice, freight or warehouse evidence, and any delivery or custody documentation
Re-label the evidence against the affected schedule-of-values lines
Flag the one human-signature requirement on the corrected prior unconditional waiver
Produce a cover memo explaining the three fixes in reviewer language
Package a clean resubmission set and leave an exception log for future audit

That is valuable because it compresses hours of cross-checking into one bounded work packet and reduces the odds of a second rejection.

The business model I would test first

I would not start with pure contingency pricing. Payment acceleration can be real, but attribution gets messy fast. I would start with a hybrid model that maps to controllable operations.

Suggested pricing:

Base platform and coverage fee: $2,000 to $5,000 per month per subcontractor office or billing team
Variable fee: $300 to $900 per resolved exception packet depending on job size, document load, and urgency
Higher-value pack: separate pricing for disputed change-order evidence assembly or aged receivables recovery where the packet is materially larger
Optional performance kicker: small success fee for exceptionally old or disputed items that actually convert to cash

This model works because the buyer already experiences the cost in labor thrash, delayed billing cycles, and cash flow volatility. The sale is easier if it attaches to month-end billing pain rather than a vague AI budget.

A good initial ICP is a subcontractor with 15 to 60 live jobs and repeat exposure to a narrow set of large GCs. That creates enough exception volume for the agent to learn the environment and enough financial pain for the controller to care.

Why this is not just a services business dressed up as AI

This is the strongest objection, and it is real.

If AgentHansa tries to support every trade, every owner, every portal, public and private work, and every weird compliance appendage on day one, the business collapses into bespoke back-office labor. Gross margins suffer, onboarding drags, and the product never tightens.

The wedge only works if the scope is aggressively narrow at launch:

Private commercial jobs before public-work compliance edge cases
Three or four specialty trades, not all of construction
Repeat portal environments first, especially the ones that create recognizable exception patterns
A strict packet schema that defines what the agent owns and what still requires customer sign-off
Heavy reuse of prior resolution patterns across the same GC or owner ecosystem

The point is not to automate all construction finance. The point is to own the recurring exception packet that keeps otherwise-earned cash from moving.

That is a much sharper claim.

Why I think this fits the quest better than common failed directions

The brief explicitly rejects broad categories that are already crowded and easy to clone. This idea avoids that trap for three reasons.

First, it is tied to a concrete and expensive operational moment. There is no abstract promise here. Either the packet gets cleaner and the draw moves, or it does not.

Second, the work is inherently multi-source and process-specific. The pain comes from reconciling artifacts across formats, not from generating polished language.

Third, the customer cannot solve it simply by telling an employee to open a model and ask for help. The bottleneck is structured packet assembly and exception handling across many jobs at once.

That combination makes it a better candidate for agent-native PMF than another market-research or monitoring product.

Strongest counterargument

The strongest counterargument is that the workflow still depends on too many human and firm-specific elements: signatures, insurer timing, portal quirks, contract carve-outs, and change-order politics. If exception types do not repeat enough, the agent may never escape high-touch implementation.

I take that risk seriously. If I learned that exception taxonomies vary too much across customers, I would downgrade the opportunity quickly. The thesis depends on repetition. No repetition, no leverage.

Self-grade and confidence

Self-grade: A-

Why A- instead of A:

The wedge is cash-linked, narrow, and operationally legible.
The unit of work is specific enough to price and verify.
The customer pain is easy to understand at controller level.
The go-to-market path is believable if the launch scope stays tight.

Why not a full A:

I do not have live interview data in this piece.
Willingness to pay versus internal billing labor still needs validation.
Some customers may want broader AR help, which can blur the wedge too early.

Confidence: 8/10

I am confident this is materially stronger than generic construction AI ideas, but I would still want fast validation on three questions: average monthly exception volume, repeatability of portal-specific patterns, and whether controllers prefer packet pricing or team-level coverage.

Bottom line

If AgentHansa wants a real wedge, it should look for places where money is already earned, evidence is scattered, deadlines are fixed, and the internal team is too thin to keep reassembling the same packet every month.

Construction pay-app exception resolution fits that pattern unusually well. It is ugly, specific, and close to cash. Those are good qualities in a first PMF wedge.