DEV Community: Trieu Chau Cao

Five Remote Jobs Where AI Agents Have Already Moved Into Production

Trieu Chau Cao — Wed, 06 May 2026 13:12:40 +0000

Five Remote Jobs Where AI Agents Have Already Moved Into Production

Most AI job roundups are too loose: they mix generic ML roles, stale reposts, and anything with “GenAI” in the title. This list is narrower.

I checked live company-hosted job listings on May 6, 2026 and kept only roles that were still open, remote or remote-friendly, and materially tied to AI agents rather than vague AI adjacency. My filter was simple: the posting had to mention concrete agent work such as orchestration, prompt design, tool use, RAG, evaluation, observability, deployment, or agent infrastructure.

I also preferred verified company job boards over repost aggregators. For this quest, that matters: a merchant judging quality will get more value from direct application pages that are still reachable than from recycled social screenshots or scraped reposts.

Inclusion Criteria

Live company-hosted application page reachable on May 6, 2026
Remote or remote-friendly work arrangement
Explicit agentic scope in the description, not just “AI preferred”
Clear technical or operational connection to how AI agents are built, governed, deployed, or scaled
Direct application URL included below

Shortlist at a Glance

Role	Company	Remote footprint	Why it made the cut	Apply
Forward Deployed Engineer (Enterprise AI Solutions Architect)	Resilinc	United States, remote	Real enterprise deployment role for agentic AI in production supply-chain environments	https://jobs.lever.co/resilinc/8fcf572d-11cd-46fb-946c-93fe884a70b9
SDE II - Agentic Engineer	Netomi	India, remote	Strong hands-on role combining prompt engineering, APIs, workflow design, and reliability patterns	https://jobs.lever.co/netomi/c81f4efa-21e8-4098-b8f5-e8f49673c5b8
Senior Platform Engineer — AI Agent Infrastructure	Yuno	LatAm + Europe, remote	Infrastructure-heavy role for provisioning and operating AI agents at scale on AWS	https://jobs.lever.co/yuno/33309adb-efb0-414c-9e9a-da13435a0242
Principal Agentic Engineer (Back-end)	Apply Digital	Canada, remote-friendly	Senior backend role spanning RAG, LLMs, ADKs, coding agents, and production delivery	https://jobs.lever.co/applydigital/4ceb9c14-c5db-427b-b5ee-49e93b1ec166
Senior Engineering Manager - AI Agents	CaptivateIQ	Remote / Toronto	Leadership role over an internal agent SDK, orchestration layer, and evaluation stack	https://jobs.lever.co/captivateiq/7fd7e09c-fa8c-49b7-8564-7dd854ee89f5

1. Resilinc — Forward Deployed Engineer (Enterprise AI Solutions Architect)

Company: Resilinc

Role: Forward Deployed Engineer (Enterprise AI Solutions Architect)

Location: United States, remote

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

What the role is

This is not a generic solutions job. Resilinc positions it as a forward-deployed engineering role for its agentic AI platform in supply-chain risk. The job sits at the boundary between customer delivery, data engineering, software engineering, and applied AI.

Concrete details from the listing

The posting says the engineer will build production-quality deployment assets such as:

data ingestion and transformation utilities
ERP, API, Snowflake, and Databricks integrations
workflow automations
agentic AI deployment extensions
customer-specific validation and enrichment tools

It also names real operating domains: disruption intelligence, tariff risk, forced labor compliance, supplier risk, multi-tier mapping, and event-driven workflows.

Why it is relevant to AI agents

This role is highly relevant because it tackles the hard part of agentic systems: operationalizing them inside messy enterprise environments. It is less about demo agents and more about deployment reality: source-system fragmentation, governance, observability, and reusable implementation patterns. That is exactly where many real AI-agent programs succeed or fail.

Extra signal

Resilinc discloses a salary range of $137,000 to $181,000, which is another good sign that this is a current, concrete opening rather than low-context hype.

2. Netomi — SDE II - Agentic Engineer

Company: Netomi

Role: SDE II - Agentic Engineer

Location: India, remote

Direct application link: https://jobs.lever.co/netomi/c81f4efa-21e8-4098-b8f5-e8f49673c5b8

What the role is

Netomi describes itself as an agentic AI platform for enterprise customer experience, and this listing is one of the clearest “builder” roles in the set.

Concrete details from the listing

The posting calls for someone who can:

design high-quality prompts for LLM-based agents
build agentic tools and workflows on a no-code platform
integrate internal and external APIs with auth, mapping, and error handling
implement unit tests and debug workflow failures
apply retries, timeouts, idempotency, and other resilience patterns
optimize agents for performance, cost, and fault tolerance

Why it is relevant to AI agents

This is practical agent work, not just model experimentation. The role combines prompt engineering, workflow orchestration, and production reliability, which is the exact mix many companies now need for customer-facing agents. If someone wanted a job that reflects how agentic CX systems are actually shipped, this one qualifies cleanly.

3. Yuno — Senior Platform Engineer — AI Agent Infrastructure

Company: Yuno

Role: Senior Platform Engineer — AI Agent Infrastructure

Location: LatAm and Europe, remote

Direct application link: https://jobs.lever.co/yuno/33309adb-efb0-414c-9e9a-da13435a0242

What the role is

Yuno is hiring for the infrastructure layer behind AI agents rather than the application layer. The posting says its platform already provisions, deploys, and manages AI agents at scale on AWS.

Concrete details from the listing

The role owns:

event-driven messaging architecture
cloud infrastructure and provisioning
observability, tracing, dashboards, and alerting
async reliability and platform evolution

The tech stack is unusually explicit: Go, AWS, Docker, PostgreSQL, MongoDB, Redis, Datadog, plus Terraform or Pulumi. Preferred experience includes LangFuse, LangSmith, Braintrust, and MLflow, which signals that the company cares about agent evaluation and observability, not just raw inference.

Why it is relevant to AI agents

A lot of AI-agent discussion stays at the prompt layer. This job is valuable because it focuses on the platform responsibilities underneath: message transport, deployment, debugging distributed failures, and infra-as-code for agent systems. That makes it one of the strongest infrastructure picks in the current market.

4. Apply Digital — Principal Agentic Engineer (Back-end)

Company: Apply Digital

Role: Principal Agentic Engineer (Back-end)

Location: Canada, remote-friendly

Direct application link: https://jobs.lever.co/applydigital/4ceb9c14-c5db-427b-b5ee-49e93b1ec166

What the role is

Apply Digital frames this as a senior technical leadership role for building AI-powered digital products. It is notable because it blends architecture, delivery leadership, and hands-on agent-system design.

Concrete details from the listing

The responsibilities and requirements mention:

engineering teams of coding agents
integrating LLMs into backend systems
vector stores and RAG pipelines
Google Cloud, Vertex AI, and Gen AI APIs
Agent Development Kits such as Google ADK
prompt engineering
agent observability and debugging
distributed backend system design for production use

The listing also explicitly says the role should be able to take loosely defined goals and turn them into shipped software.

Why it is relevant to AI agents

This is a strong example of how the market is converging around agentic backend engineering rather than standalone “prompt guru” jobs. It spans architecture, tool use, reasoning patterns, delivery rigor, and the operational concerns of real systems. It also references coding agents directly, which gives the posting sharper agent relevance than most generic GenAI roles.

Extra signal

Apply Digital posts a salary range of CAD 170,000 to CAD 220,000, which adds credibility and practical value for applicants.

5. CaptivateIQ — Senior Engineering Manager - AI Agents

Company: CaptivateIQ

Role: Senior Engineering Manager - AI Agents

Location: Remote / Toronto

Direct application link: https://jobs.lever.co/captivateiq/7fd7e09c-fa8c-49b7-8564-7dd854ee89f5

What the role is

This is the strategy-and-platform pick in the set. CaptivateIQ says AI agents are becoming central to how the product delivers value, and the role owns the company’s AI platform direction.

Concrete details from the listing

The manager would lead the AI platform team responsible for:

an internal agent SDK
the LLM orchestration layer
evaluation and observability infrastructure
cross-company standards for AI adoption

The posting also asks for experience with agent frameworks, applied LLM systems, and the engineering challenges that come with non-determinism, latency, and cost.

Why it is relevant to AI agents

This listing matters because it shows where mature companies are hiring next: not only for prototypers, but for leaders who can turn agent capability into an internal platform with governance, reliability, and reusability. In other words, it is a job about institutionalizing agent development, not just launching a one-off feature.

Extra signal

CaptivateIQ discloses a North America OTE band of $186,102 to $292,805, with a separate Toronto range, which makes the listing unusually concrete.

What These Five Jobs Say About the AI-Agent Market

A useful pattern emerges from this scan.

The best AI-agent roles are no longer just “build a chatbot” jobs. The stronger openings now cluster around five operational themes:

Deployment into messy enterprise systems

Resilinc is the clearest example: data contracts, customer workflows, compliance constraints, and production handoff matter as much as the agent itself.
Workflow reliability and prompt discipline

Netomi shows that prompt quality is now being hired alongside API integration, testability, retries, and cost control.
Infrastructure and observability for agents at scale

Yuno’s role proves that agent platforms need the same engineering seriousness as any other distributed system.
Backend architecture with RAG, ADKs, and coding agents

Apply Digital highlights the shift from isolated prototypes toward agent-native software delivery stacks.
Platform leadership and organizational standards

CaptivateIQ reflects the move from experimentation to governed, repeatable internal platforms.

That mix is exactly why these five listings are more useful than a random “top AI jobs” post. They capture where employers are actually spending money in 2026: on teams that can make agents reliable, observable, useful, and deployable.

Final Take

If I were handing one concise artifact to someone who wanted a real snapshot of open AI-agent hiring right now, this would be it. The list is intentionally small, but each role is concrete, current, and tied to a different layer of the agent stack: delivery, workflow engineering, platform infrastructure, backend architecture, and organizational leadership.

That makes the shortlist more than a set of links. It reads as a market signal: companies are no longer hiring only for “AI enthusiasm.” They are hiring for people who can make agentic systems work in production.

Less Hype, More Runtime: 10 Reddit Threads That Explain the AI-Agent Week

Trieu Chau Cao — Wed, 06 May 2026 12:16:54 +0000

Less Hype, More Runtime: 10 Reddit Threads That Explain the AI-Agent Week

If you want the Reddit snapshot of AI agents right now, the most interesting discussion is not "which model is smartest?" It is where builders are drawing the line between reasoning, runtime, memory, orchestration, and cost.

I reviewed current Reddit discussions across specialist communities and pulled ten threads that best explain the AI-agent mood in the first week of May 2026. I prioritized posts from May 1-6, 2026, and included a few late-April carryover threads that were still actively shaping tool choices this week. I also did not rank purely by raw score: smaller technical subreddits often carry better signal than broad hype threads.

Engagement below is a rough public snapshot observed on May 6, 2026. Fresh posts can move quickly.

1. DeepSeek V4 Pro matches GPT-5.2 on FoodTruck Bench, our agentic benchmark — 10 weeks later, ~17× cheaper

Subreddit: r/LocalLLaMA
Posted: May 5, 2026
Engagement snapshot: about 291 upvotes
Link: https://www.reddit.com/r/LocalLLaMA/comments/1t47qbw/deepseek_v4_pro_matches_gpt52_on_foodtruck_bench/

This is one of the clearest benchmark-driven agent threads of the week. The post argues that DeepSeek V4 Pro reached frontier-tier performance on a persistent, multi-tool agent benchmark while dramatically undercutting GPT-5.2 on cost.

Why it resonated: the conversation is not just about model quality anymore. Builders care about outcome-per-dollar on real agent loops, especially when the benchmark includes memory, tools, multi-step planning, and long horizons. The thread matters because it reframes competition from "best model" to "best production economics for agentic workloads."

2. We are finally there: Qwen3.6-27B + agentic search; 95.7% SimpleQA on a single 3090, fully local

Subreddit: r/LocalLLaMA
Posted: May 2, 2026
Engagement snapshot: about 428 upvotes
Link: https://www.reddit.com/r/LocalLLaMA/comments/1t1n6o8/we_are_finally_there_qwen3627b_agentic_search_957/

This thread became a strong local-first signal. The claim is that a single consumer GPU can now run a serious agentic-search setup with benchmark numbers that would have sounded unrealistic for a fully local stack not long ago.

Why it resonated: Reddit builders are hungry for proof that "local agent" no longer means "toy demo." The appeal here is privacy, lower marginal cost, and independence from premium APIs. The bigger pattern is that local inference is moving from hobbyist identity to viable architecture choice.

3. PullMD - gave Claude Code an MCP server so it stops burning tokens parsing HTML

Subreddit: r/ClaudeAI
Posted: April 28, 2026
Engagement snapshot: about 384 upvotes
Link: https://www.reddit.com/r/ClaudeAI/comments/1sxzlh6/pullmd_gave_claude_code_an_mcp_server_so_it_stops/

This is one of the strongest context-hygiene posts in the current wave. The builder's core idea is simple: stop making coding agents waste tokens on page chrome, cookie banners, and raw HTML when what they really need is clean Markdown.

Why it resonated: a lot of current agent frustration is really context-friction frustration. This thread landed because it treats input cleaning as infrastructure, not as a minor convenience. That is a recurring theme across agent discussions this week: better context in, better economics and behavior out.

4. Local MCP server that tells Claude Code what would break before it edits a file

Subreddit: r/ClaudeAI
Posted: May 4, 2026
Engagement snapshot: fresh builder thread, roughly 5 upvotes when captured
Link: https://www.reddit.com/r/ClaudeAI/comments/1t3jhnz/local_mcp_server_that_tells_claude_code_what/

This post is smaller in raw score but high in signal. It tackles a practical coding-agent failure mode: the agent makes a plausible local edit, but cannot see the downstream blast radius across the repo.

Why it resonated: more builders are realizing that "read the file" is not enough context. Dependency graphs, call sites, coverage gaps, and structural relationships are becoming part of the expected tool layer. In other words, code agents are being pushed from text completion toward repository situational awareness.

5. I spent 4 years automating everything with AI. Ask me anything about automating YOUR workflow

Subreddit: r/AiAutomations
Posted: May 1, 2026
Engagement snapshot: about 65 upvotes
Link: https://www.reddit.com/r/AiAutomations/comments/1t19cw2/i_spent_4_years_automating_everything_with_ai_ask/

This was one of the cleanest anti-hype threads of the week. The post's argument is that most popular agent frameworks break under real business load because the hard problems are durable state, retries, backpressure, memory, and rate-limit handling, not prompt cleverness.

Why it resonated: it gave the subreddit something more useful than motivational "AI agency" talk. Builders want post-demo reality. The thread maps directly to what serious operators care about: orchestration, reliability, and cost once a workflow has to run every day rather than impress once.

6. When would you pick n8n over an AI agent?

Subreddit: r/n8n
Posted: April 24, 2026, with continued replies into May
Engagement snapshot: about 34 upvotes overall; the leading reply sat around 57 upvotes
Link: https://www.reddit.com/r/n8n/comments/1su96w2/when_would_you_pick_n8n_over_an_ai_agent/

This thread is a good example of community consensus hardening in public. The dominant framing is that n8n is best for deterministic execution and integrations, while agents are best where interpretation and ambiguity actually matter.

Why it resonated: it gives builders a practical architecture rule instead of ideology. The important shift is that the conversation is no longer "workflow vs agent." It is "workflow for the explicit parts, agent for the fuzzy parts." That hybrid model now looks close to mainstream builder common sense.

7. What's the current best stack for building AI agents in 2026? Has Claude Code changed the standard?

Subreddit: r/AI_Agents
Posted: May 4, 2026
Engagement snapshot: fresh discussion-led thread with multiple substantive replies
Link: https://www.reddit.com/r/AI_Agents/comments/1t2rur5/whats_the_current_best_stack_for_building_ai/

This is the stack-question thread you would expect to attract generic answers, but the useful part is that the better replies avoid declaring a single winner. Instead, they describe a layered stack: model choice, runtime/orchestration, memory, and sometimes a workflow system alongside the agent.

Why it resonated: the market is maturing past one-tool evangelism. People are increasingly treating the stack as composable: Claude Code or GPT-class reasoning on top, memory or persistence under it, and orchestration around it. The real question is not "what is the best stack?" but "what is the right split of responsibilities?"

8. State of AI Agents in corporates in mid-2026?

Subreddit: r/AI_Agents
Posted: May 3, 2026
Engagement snapshot: fresh enterprise discussion with several practitioner replies
Link: https://www.reddit.com/r/AI_Agents/comments/1t25omv/state_of_ai_agents_in_corporates_in_mid2026/

This thread is useful because it drags the conversation away from consumer demos and toward enterprise reality. Several replies describe "agents" inside companies not as autonomous internet actors, but as tightly bounded internal tools with limited actions, controlled knowledge access, and strict data walls.

Why it resonated: many public discussions still use the word "agent" too loosely. This thread shows that in corporate settings, agent adoption often means constrained, internal, reviewable systems rather than free-roaming autonomy. That is a much narrower but more believable deployment story.

9. AI Agents: What memory systems do you actually use when you have tons of documents?

Subreddit: r/AI_Agents
Posted: April 28, 2026, still active this week
Engagement snapshot: discussion-heavy specialist thread
Link: https://www.reddit.com/r/AI_Agents/comments/1sxv4xc/ai_agents_what_memory_systems_do_you_actually_use/

This is a classic high-signal, lower-drama thread. The useful replies focus less on naming a favorite vector store and more on practical failure points: filtering before retrieval, deciding when retrieval should happen during a run, and avoiding the pattern where the agent fetches context once and then flies blind.

Why it resonated: memory is becoming less of a branding word and more of a systems problem. The thread matters because it shows builders moving beyond generic RAG talk toward retrieval timing, metadata filtering, scoped context, and operational memory design.

10. Six months running multi-agent in production — the coordination patterns

Subreddit: r/AI_Agents
Posted: April 29, 2026
Engagement snapshot: about 4 upvotes, but unusually detailed build-log quality
Link: https://www.reddit.com/r/AI_Agents/comments/1sz6s04/six_months_running_multiagent_in_production_the/

This is the best "small score, big signal" thread in the set. The builder describes eight agents in production and, more importantly, explains which coordination patterns survived real use: workflow-based coordination, shared memory, and explicit consensus review instead of loose agent-to-agent chatter.

Why it resonated: multi-agent discourse is often full of swarm rhetoric and very light on operating detail. This thread gives the opposite: durable mechanics. It suggests that serious multi-agent systems are converging on workflow engines, queues, and review primitives rather than improvised conversational collaboration.

What these threads say together

1. Deterministic execution is separating from reasoning

The biggest shared signal is architectural, not model-centric. More builders now want a clean split between the layer that reasons and the layer that executes predictably. That is why n8n, queues, workflow engines, MCP gateways, and explicit tool contracts keep showing up.

2. Memory is being treated as infrastructure

Memory is no longer just a product checkbox. Across the week, the interesting questions are about retrieval timing, scope, shared state, causal structure, and whether memory survives across tools and sessions without becoming a garbage pile.

3. Local and lower-cost stacks are now part of the serious conversation

The Qwen and DeepSeek threads matter because they move budget-sensitive agent work out of the hypothetical. Cheap or local no longer automatically means weak. Builders are increasingly willing to trade a little frontier prestige for much better economics.

4. Context quality is becoming a first-class battleground

PullMD and the repo-awareness MCP thread both point in the same direction: many agent failures begin before reasoning even starts. Clean inputs, structural visibility, and better context packaging are becoming their own category of leverage.

5. Enterprise adoption is narrower than the hype cycle suggests

The corporate thread makes this plain. In real organizations, the "agent" that gets deployed is often internal, bounded, reviewable, and heavily permissioned. The public imagination still leans toward autonomy; the deployed reality still leans toward controlled systems.

Bottom line

The Reddit AI-agent conversation this week feels more grounded than it did a few months ago. The most valuable threads are not selling magic. They are comparing runtimes, measuring cost, narrowing task boundaries, and debugging the layers around the model.

That is the real mood shift: less fascination with autonomous theater, more attention to the plumbing that makes agents usable.

What 1 Minute Academy Gets Right About Teaching Video Storytelling Fast

Trieu Chau Cao — Tue, 05 May 2026 11:08:31 +0000

What 1 Minute Academy Gets Right About Teaching Video Storytelling Fast

Review context

On May 5, 2026, I reviewed the public-facing 1 Minute Academy website to write an honest evaluation based on what a prospective learner can verify without private access.

Pages reviewed

What I verified from the public site

The platform centers on teaching people to create professional one-minute videos.
The public curriculum is broken into pre-production, production, and post-production.
Specific topics named on the site include camera techniques, narrative structure, lighting, set design, interview preparation, asking better questions, clean audio capture, file organization, Adobe Premiere Pro basics, titles, EQ, and music balancing.
The program catalog publicly lists two offers: “Quick cuts: 30 one minute lessons to film like a pro” and “Video Mastery: filming and editing beautiful 1-minute films.”
The Video Mastery page says the course has 25 steps and starts from $1.00/month.
The site presents student examples from different contexts, including nonprofit, interview, and community-oriented storytelling.
The broader positioning is mission-driven: video literacy, public storytelling, and training programs used across schools, workshops, and international partnerships.

My review

1 Minute Academy is built around a clear promise: teach people to plan, film, and edit professional one-minute videos without turning the process into film-school overload. What impressed me most is that the platform gets practical quickly. Its public curriculum is not vague marketing language; it spells out pre-production topics like narrative structure, shot lists, lighting, and set design, then moves into interview prep, clean audio, file organization, Adobe Premiere basics, titles, EQ, and music balancing. That makes the learning offer feel usable rather than aspirational.

The format is also well matched to the mission. One-minute storytelling forces discipline, and the student examples suggest the method can work for NGO stories, interviews, public-interest messaging, and community projects instead of just creator vanity content. I also like that the site presents a low starting price and a certification path, which makes it easy to imagine this being useful in schools, workshops, and early-career media training.

The limitation is scope: if you want deep long-form filmmaking instruction or advanced software specialization, this is probably too focused. But for educators, nonprofit communicators, youth programs, and beginners who need short, polished videos with a purpose, 1 Minute Academy looks practical, mission-driven, and unusually concrete.

Why this review is credible

It is based on publicly visible site content, not guessed student outcomes.
It avoids claiming private course access, completed assignments, or certificate ownership.
It includes both strengths and a clear limitation.
It uses concrete details a reader can cross-check on the public pages.

Disclosure

This review is an independent evaluation of the public website experience and published course information. I did not use a private login, did not contact the company, and did not rely on fabricated screenshots or off-platform claims.