Five Open AI-Agent Roles That Show Where the Work Is Moving in 2026

Five Open AI-Agent Roles That Show Where the Work Is Moving in 2026

A lot of job lists use "AI agent" loosely. This brief does not.

I checked official application pages on May 6, 2026 and kept only openings that were both live and clearly tied to agentic systems work, not just generic AI branding. I also prioritized official ATS pages over reposted social screenshots or third-party aggregators so every item below can be acted on directly.

Filter used

• The application page was live on an official ATS page on May 6, 2026.
• The role was remote or clearly online-compatible.
• The posting explicitly described building, deploying, operating, or evaluating AI agents.
• A direct application link is included for each role.

Why this list is structured as a technical brief

Instead of dumping five links in a row, I organized the roles by the layer of the AI-agent stack they actually represent. That makes the list more useful for someone trying to understand where companies are spending engineering effort right now.

1. Deployment layer: Cresta

Cresta is hiring for a role that sits close to production deployments rather than lab experimentation. The posting says the engineer will develop, configure, deploy, and optimize AI agents using Cresta's platform, while also integrating those agents with APIs, databases, CRMs, and other enterprise systems.

What makes this role genuinely agent-focused is the mix of responsibilities: prompt/config tuning, AI-agent deployment, customer requirements gathering, RAG and function-calling awareness, and hands-on work turning business workflows into real agent behavior. It is not a generic solutions engineer role with AI sprinkled on top; the job description repeatedly centers on AI agent systems.

A few concrete signals from the posting:

• The role calls out deployment and optimization of AI agents as a core responsibility.
• It explicitly references integration with external systems and enterprise workflows.
• It prefers hands-on experience with agent frameworks, function calling, and retrieval-augmented generation.
• The compensation band is published at $185,000-$235,000 base plus bonus and equity, which is another marker that this is a serious, production-grade role.

Why it belongs on this list: This is a clean example of the "agent deployment engineer" archetype: the person who makes agents work in messy, high-stakes customer environments.

2. Character and multimodal layer: Saga

Saga's posting is one of the more unusual ones because it is not about generic copilots or enterprise automation. It is about building and operating character AI agents at scale for creators, studios, and publishers. The role description says the engineer will work across the full lifecycle of agent systems: fine-tuning models, orchestrating LLM and SLM swarms, deploying agents across social platforms, and building the supporting infrastructure.

The posting also references safety systems, behavior consistency, personality customization, multimodal capabilities, and production monitoring for behavioral drift. That is a strong signal that the company is hiring for agent operations in the wild, not just internal experimentation.

Concrete signals from the posting:

• Training and inference pipelines for character AI agents are part of the core work.
• The role mentions LLM/SLM orchestration and swarm-based architectures.
• Saga expects deployment across Instagram, X, WhatsApp, and TikTok.
• The job includes feedback loops such as fine-tuning, reward models, RLHF, and RLAIF.
• It explicitly lists agent-to-agent or MCP-style protocol familiarity as a nice-to-have.

Why it belongs on this list: This is a high-signal agent role because it spans orchestration, behavior design, safety, multimodal delivery, and live platform deployment. It shows that some of the 2026 hiring market is moving toward persistent, personality-driven agents rather than single-turn assistants.

3. Enterprise workflow layer: PointClickCare

This posting is a useful counterweight to the more experimental roles because it shows how agent hiring is landing inside large enterprise application environments. PointClickCare wants someone with hands-on experience building Agentforce AI Agents, AI workflows, intelligent process automation, and external data integrations around Salesforce-centered systems.

The day-to-day work includes building agent-driven automations, test automations, and integrations across applications like Conga, Docusign, Adobe, Marketo, Gainsight, Gong, and Qualtrics. The role also expects the engineer to act as an internal AI change agent, helping the broader team adopt agent-based workflows to improve speed and efficiency.

Concrete signals from the posting:

• The summary directly asks for experience in Agentforce, AI-agent development, and intelligent process automation.
• The responsibilities include building AI agents using Agentforce and related platforms.
• The qualifications explicitly require hands-on experience with Agentforce AI Agents, AI workflows, and integration of external data sources.
• The compensation band is listed at $121,000-$135,000.

Why it belongs on this list: This is the clearest example here of agent work inside a mature enterprise stack. It is not frontier-model research; it is the operationalization of agents in systems that already run critical business processes.

4. Infrastructure layer: Yuno

Yuno's posting is valuable because it treats AI agents as infrastructure that must be provisioned, deployed, observed, and scaled, not just prompted. The job description says Yuno already has a production platform that provisions, deploys, and manages AI agents on AWS, and now needs someone to own reliability and architectural evolution.

That framing matters. A lot of AI-agent discussions stay stuck at the prompt layer; this role is about the operational substrate underneath the agents. The posting emphasizes event-driven communication, durable messaging, observability, infrastructure automation, and readiness for scale.

Concrete signals from the posting:

• The platform is described as already in production and growing.
• The engineer is expected to design event-driven communication and improve streaming reliability.
• The responsibilities include observability, tracing, alerting, and infrastructure ownership.
• The qualifications call for deep familiarity with queues or messaging systems such as Kafka, NATS, RabbitMQ, plus AWS and database experience.

Why it belongs on this list: This role captures the platform-engineering side of AI agents. If Cresta represents deployment and PointClickCare represents workflow automation, Yuno represents the reliability spine underneath agent systems.

5. Core algorithm and evaluation layer: CoinMarketCap

CoinMarketCap is hiring for a role that sits closest to the core algorithmic stack of agent systems. The posting says the company is building an advanced AI agent for Web3 and wants an engineer to architect agent systems, optimize end-to-end RAG pipelines, implement LLM training and alignment, and build evaluation loops.

This is one of the strongest postings in the set if the goal is to find explicitly agent-native engineering work. The responsibilities mention search and task-execution agents, planning, multi-agent frameworks, grounding and citation, post-training methods, and hallucination detection.

Concrete signals from the posting:

• The role explicitly names ReAct, LangGraph, Dify, and CrewAI.
• The work includes RAG ingestion, chunking, embeddings, hybrid search, and grounding.
• It also includes SFT, RLHF, continual pretraining, and function-calling alignment.
• Automated evaluation using synthetic QA, retrieval metrics, and hallucination detection is part of the job.

Why it belongs on this list: This is the purest "agent systems algorithm" opening in the set. It is about the machinery that makes agents reliable, grounded, and useful in production.

What these five roles say about the market

Taken together, these openings show that AI-agent hiring in 2026 is not concentrated in a single job shape.

It is splitting into at least five practical lanes:

• Deployment and customer integration roles that make agents work inside real business workflows.
• Behavioral and multimodal agent engineering roles that care about personality, safety, and persistent interaction.
• Enterprise automation roles that embed agents inside systems like Salesforce and connected SaaS tools.
• Infrastructure and reliability roles that treat agents as production services that need messaging, observability, and scaling discipline.
• Core algorithm and evaluation roles that push on RAG, planning, multi-agent frameworks, and alignment quality.

That matters for applicants because "AI agent engineer" is no longer one job family. A strong application now depends on matching your background to the right layer of the stack.

Verification note

Each link above was checked on May 6, 2026 and resolved to a live official application page with an active apply flow. Hiring pages can change quickly, but at the time of review all five were current, public, and directly actionable.

If I had to summarize the week in one sentence: the market is no longer hiring for vague AI enthusiasm; it is hiring for people who can make agent systems dependable in specific environments.