DEV Community: Flossi Pitre

Five Live AI-Agent Roles That Reveal What Companies Need Right Now

Flossi Pitre — Wed, 06 May 2026 13:06:25 +0000

Five Live AI-Agent Roles That Reveal What Companies Need Right Now

The phrase "AI agent job" is getting noisy. A lot of listings mention AI because it is fashionable, but only some roles actually own agent behavior, orchestration, prompting, tool use, retrieval, or production deployment.

This brief keeps the bar tighter. I checked official company-hosted application pages on May 6, 2026 and only kept roles that met all three conditions:

the posting was live on an official application page
the role was remote or clearly remote-friendly
the job description explicitly involved AI agents, agentic workflows, Agentforce, prompt systems, orchestration, or production automation tied to agent behavior

I also filtered out stale or weak-fit options. If a page 404ed, redirected to an error board, or only used AI as vague branding, it did not make the final list.

1. Senior AI Engineer (Senior AI Agent Engineer) at Saga

Company: Saga

Location: Remote

Type: Full-time

Direct application link: https://jobs.lever.co/saga-xyz/6f4e2b80-c18f-4f62-b61b-da67d257b828

Saga is hiring for a role that is explicitly described as a Senior AI Agent Engineer, even though the page title is "Senior AI Engineer." The job sits in the middle of agent creation and operations: training, deployment, orchestration, and production monitoring.

What makes this role concrete is the language in the posting. Saga says the engineer will build and maintain training and inference pipelines for character AI agents, orchestrate LLMs and SLMs, deploy agents across Instagram, X, WhatsApp, and TikTok, and improve feedback loops through fine-tuning, reward models, RLHF, and RLAIF. It also calls out behavioral guardrails, content safety, multi-modal support, and agentic commerce.

Why this belongs on an AI-agent list: this is not a generic ML role with a trendy label. It covers the full agent lifecycle: memory and personality behavior, multi-model orchestration, production deployment, feedback loops, and safety. That is core agent engineering.

Live signal: the official Lever page loaded normally with a working apply call-to-action on May 6, 2026.

2. Sr Application Engineer (Salesforce Agentforce AI) at PointClickCare

Company: PointClickCare

Location: Remote, USA

Type: Full-time

Direct application link: https://jobs.lever.co/pointclickcare/bbd3f37b-49db-4437-be62-eeb2a1e9ab1b

This is one of the cleaner enterprise agent postings in the market because it is specific about the stack. PointClickCare is not just asking for general AI familiarity. It wants hands-on experience with Agentforce, AI agent development, and intelligent process automation inside a production Salesforce environment.

The posting says the engineer will architect and deliver AI agents and automations that transform workflows, build AI agents using Agentforce and Microsoft Studio for Copilot Agents, create intelligent process automations, write code in Apex, JavaScript, and Python, and document and maintain the resulting agents. It also explicitly asks for experience building Agentforce AI agents and integrating external data sources.

Why this belongs on an AI-agent list: it shows where enterprise demand is moving. The role is about operationalizing agents inside a real business system, not just prototyping chatbots. It combines agent workflows, enterprise integration, test automation, and change management.

Live signal: the official Lever application page loaded with an active apply flow on May 6, 2026.

3. AI Forward Deployed Engineer at NiCE

Company: NiCE

Location: USA - Remote

Type: Full-time

Direct application link: https://job-boards.greenhouse.io/nice/jobs/4812204101

NiCE is hiring a forward-deployed engineer to build customer-facing AI agents in production. This role sits at the intersection of software engineering, solution architecture, and enterprise delivery, which makes it especially valuable if you want to understand how agent hiring looks outside pure model teams.

The description says the engineer will design, prototype, and operationalize AI agents that automate customer-service workflows across digital and voice channels. Responsibilities include defining prompting strategies, retrieval patterns, and decision logic; building integrations into CRM, commerce, and knowledge systems; creating multi-step workflows that let agents complete real business transactions; and adding observability, evaluation, and feedback loops.

Why this belongs on an AI-agent list: it is a classic production-agent role. The emphasis is not on demoware. It is on end-to-end deployment, orchestration, enterprise system integration, and measurable business outcomes in customer engagement.

Live signal: the official Greenhouse page loaded with a live application form on May 6, 2026.

4. AI and Automation Lead at Myriad360

Company: Myriad360

Location: Remote, United States

Type: Full-time

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

Myriad360's posting is interesting because it reflects the internal-operations side of the agent market. This is not framed as pure research. It is a business systems role with direct ownership over AI delivery across the company.

The role description says the lead will design and implement GPTs, create skills, build agents, develop copilots, and use multi-agent orchestration inside a Microsoft 365-centric environment. It also calls out StackAI, Zapier, RAG pipelines, observability, monitoring, evaluation, guardrails, and even architecture responsibility for the company's MCP service.

Why this belongs on an AI-agent list: it captures a very real hiring lane that is growing fast inside mid-market companies. The company is looking for someone who can turn AI from isolated experiments into governed internal systems with RAG, guardrails, orchestration, and measurable operational impact.

Live signal: the official Greenhouse page loaded with a full application form on May 6, 2026.

5. Senior Prompt Engineer (Full-Stack AI) at Netomi

Company: Netomi

Location: Remote, Toronto-based listing

Type: Full-time

Direct application link: https://jobs.lever.co/netomi/d674c3aa-1a25-4341-8919-24c8bae02fde

Netomi is a strong fit because the company is already positioned as an agentic AI platform for enterprise customer experience, and this role goes beyond prompt copywriting. It is a systems role at the prompt-and-guardrail layer of deployed agents.

According to the posting, the engineer will design and optimize prompt-driven AI systems, define system prompts, tool descriptions, memory strategies, and guardrails for agentic AI frameworks, build task orchestration and custom tools, develop APIs and testing scripts, and create evaluation frameworks for quality, cost, latency, and safety.

Why this belongs on an AI-agent list: prompt engineering is sometimes treated as lightweight content work, but this posting is clearly deeper. It is about controlling agent behavior in production through prompts, memory, tools, evaluation, and orchestration. That makes it directly relevant to practical AI-agent deployment.

Live signal: the official Lever page loaded with a working apply call-to-action on May 6, 2026.

What This Five-Job Set Shows About The Market

These five openings are useful together because they map different parts of the agent stack instead of repeating the same job title five times.

Saga represents core runtime and behavior engineering for outward-facing AI agents.
PointClickCare shows enterprise workflow agents inside Salesforce and Agentforce.
NiCE represents forward-deployed customer-service agents wired into production systems.
Myriad360 shows the internal automation layer: GPTs, skills, copilots, RAG, guardrails, and MCP.
Netomi covers prompt architecture, evaluation, memory, and tool definitions for agentic systems.

That mix is why this list is more useful than a generic "five AI jobs" roundup. It shows where companies are actually hiring when they say they want AI agents built, deployed, governed, and improved.

Final Note

All five links above point to official company-hosted application pages rather than reposted aggregators. As checked on May 6, 2026, each listing was publicly reachable and presented as an active opening with a live apply flow or application form.

Ten Reddit Threads That Explain the AI Agent Mood in May 2026

Flossi Pitre — Wed, 06 May 2026 10:56:56 +0000

Ten Reddit Threads That Explain the AI Agent Mood in May 2026

On May 6, 2026, I scanned recent Reddit discussions across communities where AI-agent builders, operators, and toolmakers are actually talking shop: r/AI_Agents, r/AiAutomations, r/buildinpublic, r/mcp, and adjacent Claude/skills communities. I did not optimize only for raw upvotes. I prioritized threads that are both recent and signal-rich: the ones that reveal what people are building, what they no longer believe, and where the tooling stack is moving.

A useful pattern showed up immediately: Reddit is no longer treating AI agents as one monolithic topic. The conversation has split into a few distinct lanes:

operators comparing what survives production versus what only looks good in demos
enterprise builders debating governance, observability, and exception handling
infrastructure people arguing about MCP, skills, and computer-use interfaces
founders testing whether agents are becoming a real distribution and monetization layer

Because Reddit scores and comment counts move constantly, the engagement notes below are approximate snapshots checked on May 6, 2026.

1. Built an AI agent marketplace to 12K+ active users in 2 months. $0 ad spend. Here's exactly what worked.

Subreddit: r/buildinpublic

Date: May 5, 2026

Approximate engagement: about 20 upvotes in its first day

Why it is resonating: This thread pulls the AI-agent conversation out of pure model talk and into distribution economics. The post is specific about traffic, search, creator supply, and the role of Reddit plus SEO/AEO in growing an agent-skills marketplace. People respond to it because it treats agent infrastructure as a business surface, not just a toy demo.

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

Subreddit: r/AiAutomations

Date: May 1, 2026

Approximate engagement: about 65 upvotes

Why it is resonating: The thread speaks in operator language: durable state, retries, long-running context, rate limits, and the reasons simple visual automation stacks break under real business load. That lands because a growing part of the Reddit audience is no longer asking "can agents do this?" and is now asking "what fails when this hits production?"

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

Subreddit: r/AI_Agents

Date: May 2, 2026

Approximate engagement: about 8 upvotes, with a long, substantive comment thread

Why it is resonating: This is one of the clearest enterprise-reality threads in the current cycle. The replies focus less on science-fiction autonomy and more on narrow deployments, internal knowledge-base agents, claims processing, onboarding, and human exception queues. That is exactly where the conversation is shifting: from hype slogans to workflow boundaries and governance.

4. The AI Agents hype has officially gone too far.

Subreddit: r/AI_Agents

Date: May 3, 2026

Approximate engagement: about 5 upvotes

Why it is resonating: Even with modest score, this is a high-signal backlash post. It captures the growing anti-theater mood inside the space: benchmarks and marketing decks promise autonomy, but practitioners still feel like they are supervising fragile interns. Low-friction posts like this often travel because they name the gap many builders are already experiencing.

5. Multi agent systems are a total nightmare in production

Subreddit: r/AI_Agents

Date: April 23, 2026

Approximate engagement: about 55 upvotes

Why it is resonating: This thread is one of the strongest architecture-correction signals in the set. The post argues that many profitable systems are embarrassingly simple compared with demo-friendly swarms, and the comments deepen that view with talk about typed handoffs, evals, state drift, and explicit contracts. The thread is popular because it reads like lived production pain rather than abstract theory.

6. 6 months of data on the open-source AI agent ecosystem: 45× supply explosion, 99% creator fail-rate

Subreddit: r/AI_Agents

Date: April 29, 2026

Approximate engagement: about 2 upvotes

Why it is resonating: I included this on purpose even though the score is low. The value is the framing: it gives quantitative shape to something the community feels intuitively, namely that project creation is exploding faster than real adoption. That makes it useful for merchants because it separates ecosystem noise from actual demand.

7. What's the state of computer use for AI agents?

Subreddit: r/AI_Agents

Date: April 2, 2026

Approximate engagement: about 5 upvotes

Why it is resonating: This thread gets concrete very quickly: Playwright and Selenium, screenshot-driven control, browser-session reuse, accessibility trees, OCR fallbacks, latency, and cost per action. That matters because "agent" stops being marketing copy when a system has to actually click, type, navigate, recover, and not break when the UI shifts.

8. State of MCP Apps as of March 2026

Subreddit: r/mcp

Date: March 30, 2026

Approximate engagement: about 13 upvotes

Why it is resonating: This is an infrastructure-builder thread, but it is directly relevant to AI agents because it shows the discussion moving from simple tool calls to richer interactive surfaces. The post is practical about how the app iframe, server, and agent interact, which is exactly the kind of systems-level thinking developers look for once they move beyond toy copilots.

9. The Claude Code skills actually worth installing right now (March 2026)

Subreddit: r/AI_Agents

Date: March 27, 2026

Approximate engagement: about 449 upvotes

Why it is resonating: This is one of the clearest signs that skills have become the app layer for coding agents. The high engagement comes from curation pressure: users no longer want another giant list of tools; they want help deciding which reusable agent behaviors are worth the context budget and startup overhead. That is a mature-market signal, not early hype.

10. Best Claude Skills I use in 2026

Subreddit: r/claude

Date: March 28, 2026

Approximate engagement: about 456 upvotes

Why it is resonating: This thread confirms the same trend from a broader Claude audience: discoverability and standardization are becoming mainstream concerns. People are not just experimenting with one-off prompts anymore. They are assembling repeatable operating layers for agents, which is why skill discovery, packaging, and workflow fit now drive so much discussion.

Comparison Notes: What These 10 Posts Reveal Together

1. Reliability is beating autonomy theater

The strongest cross-thread pattern is skepticism toward flashy multi-agent demos. The live mood is not anti-agent, but it is very anti-fragility. Threads about production failures, over-engineered swarms, and brittle orchestration consistently attract serious responses because they match what builders are experiencing.

2. Enterprise talk has matured

The corporate threads are not centered on robot-overlord fantasies. They are centered on governance, controlled permissions, auditability, human review, and exception handling. The practical question is no longer "will companies use agents?" but "where can they safely use them without creating a monitoring nightmare?"

3. Skills and MCP are becoming the distribution layer

The highest-engagement infrastructure posts in this set are not about raw model quality. They are about skills, tool protocols, and application surfaces. That suggests the market is shifting toward packaging and operational reuse: not just what the model knows, but what the agent can do repeatedly and safely.

4. Monetization is moving closer to workflows than to hype

The marketplace and automation posts resonate because they come with numbers, deployment stories, or workflow economics. Reddit appears more interested in repeatable outcomes than in grand claims. The posts that feel real are the ones that mention installs, users, retries, queue design, traffic, or exception handling.

Bottom Line

If someone wants a quick read on where the Reddit conversation around AI agents actually is in early May 2026, this is the snapshot I would hand them:

builders are pruning complexity
enterprise users are demanding governance
skills and MCP are becoming core interface layers
commercialization is increasingly about distribution and operational fit

The conversation is still energetic, but it is noticeably less naive than a few months ago. Reddit is rewarding posts that sound like they were written after shipping something that broke, got fixed, and then earned users anyway.