Agentic AI Engineers Job Description Template (2026)

Why a Generic AI Engineer JD Doesn’t Work Here

Post a job description that says “AI Engineer, LLMs, Python, machine learning” and you will get a stack of resumes from computer vision specialists, MLOps engineers, and chatbot integrators who have never touched an autonomous agent loop. Agentic AI engineering is its own discipline now. It’s the difference between calling an LLM once for a completion and building a system that plans multiple steps, calls tools, checks its own output, and decides what to do next without a human in the loop for every decision.

This template is built specifically for that role. It covers the three decisions that determine whether your JD attracts engineers who can actually ship agentic systems:

• Which orchestration framework your team has standardized on, or is still evaluating
• Whether the engineer owns tool integration and function-calling reliability, evaluation, or both
• How much autonomy your agents actually have in production, and what guardrails already exist

Use the template as-is, or adapt each section to your stack.

What Agentic AI Engineers Actually Own

Agentic AI engineering sits at the intersection of software engineering and applied LLM work, and the scope shifts a lot depending on how mature your agent program already is. A team shipping its first internal agent needs someone comfortable with ambiguity. A team running agents in production against paying customers needs someone who treats reliability and cost control as first-class requirements, not afterthoughts.

Across most companies hiring for this role, the day-to-day work includes some combination of the following:

• Designing agent architectures: single-agent loops, multi-agent handoffs, or supervisor/worker patterns
• Building and hardening tool/function-calling integrations against internal APIs and third-party services
• Implementing memory and state management across multi-turn, multi-step agent sessions
• Writing evaluation harnesses that catch regressions before they reach production, not after
• Setting guardrails around what an agent is permitted to do autonomously versus what requires a human checkpoint
• Managing token cost and latency tradeoffs as agent chains grow longer

Some teams also expect the agentic AI engineer to own prompt architecture across the whole agent system, not just the individual model calls inside it.

At companies with a dedicated ML platform team, the agentic AI engineer’s scope narrows to agent logic, tool integration, and evaluation, while the platform team owns model hosting and infrastructure. At smaller companies, one engineer frequently owns the entire pipeline, from the orchestration graph to the production monitoring dashboard. Say which model applies. Engineers coming from a research-heavy ML background will read your JD differently than engineers coming from a production backend background, and the strongest agentic hires often come from the second group.

Core Technical Requirements

Write requirements around what your team actually runs, not a composite of every agent framework mentioned in the last six months of AI Twitter. Cap hard requirements at six to eight items, and for each one, state the depth you need rather than just the tool name.

Agent Orchestration Framework

Name the framework your team has adopted, and be honest about how settled that decision actually is. “2+ years building stateful multi-step agents with LangGraph, including custom state graphs and conditional routing” is a real requirement. “Experience with AI agents” only names a category. If your team runs CrewAI, specify whether the engineer needs experience with role-based crew design or just task delegation. If you’re on the OpenAI Agents SDK or a comparable handoff-based framework, note whether the engineer needs experience with structured handoffs between specialized sub-agents. Teams still evaluating frameworks should say so directly. That honesty attracts engineers who can help make the decision, not just execute one someone else already made.

Tool and Function-Calling Reliability

This is the requirement most JDs skip entirely, and it’s usually the hardest part of the job. An agent that calls the wrong tool, passes malformed arguments, or retries a failed call indefinitely will break production faster than almost any other failure mode. Require experience with structured output validation (JSON schema enforcement, Pydantic models), retry and fallback logic for tool failures, and rate-limit-aware integration against internal or third-party APIs. If your agents call internal microservices, say so, since that’s a materially different integration surface than calling public APIs like search or a weather service.

Evaluation and Observability

Non-deterministic systems need a different testing discipline than deterministic code, and this is where a lot of otherwise-strong software engineers struggle on their first agentic role. Specify the evaluation tooling your team uses (LangSmith, Langfuse, Braintrust, or an internal eval harness) and whether the engineer will build eval suites from scratch or extend existing ones. If you track agent behavior against a golden dataset or use LLM-as-judge scoring, name that explicitly. Engineers who have only shipped agents without a real evaluation loop tend to underestimate how much of this job is instrumentation and regression testing rather than prompt writing.

Retrieval and Memory Systems

If your agents pull context from a knowledge base, name the vector store (Pinecone, Weaviate, pgvector, or another) and specify whether the engineer owns retrieval quality tuning or just the integration layer. Distinguish retrieval-augmented context from actual agent memory, meaning persisted state across sessions, since these are frequently confused in job postings and they require different skills. An engineer who has only built a RAG chatbot has not necessarily built an agent with durable memory across multi-day tasks.

Nice-to-Have Skills

Keep the preferred list to three or five items. A longer preferred list reads as a second requirements section and filters out strong candidates who don’t check every box on a list that was never meant to be a hard gate.

Genuine preferences worth including for an agentic AI engineering role:

• Experience with guardrail frameworks (Guardrails AI, NeMo Guardrails, or a custom policy layer)
• Prior work on multi-agent coordination patterns, including supervisor/worker or debate-style architectures
• Fine-tuning or distillation experience for cost or latency optimization
• Familiarity with prompt injection defenses and sandboxed tool execution for agent security
• Contributions to an open-source agent framework, or a public agent project a hiring manager can actually inspect

Skip requiring a specific certification here. The field moves too fast for credentialing to mean much yet, and a working GitHub repo tells you more about an agentic AI engineer’s real skill than any credential currently available.

The Full JD Template

The template below targets a senior agentic AI engineer role for a nearshore or remote hire. Customize the bracketed fields for your stack, and cut any section that doesn’t apply rather than leaving it generic.

Compensation Ranges

Agentic AI engineering carries a real specialization premium over general software engineering roles right now, since the discipline is young and production-tested experience is scarce. Publishing a range in your JD shortens time-to-offer by filtering out mismatched expectations before the first screening call. The ranges below reflect 2026 all-in placement costs for nearshore agentic AI engineers hired through Kore BPO. Full salary data by experience level is available in the Nearshore Agentic AI Engineers Salary Guide.

These ranges cover salary, benefits administration, and account management support, with no upfront search fees. All-in costs run 45 to 65% below the equivalent US-based senior agentic AI engineer role, where total compensation for comparable experience currently lands between $155,000 and $210,000 annually in most major US tech markets.

Remote-Ready JD Considerations

Agentic systems fail differently than traditional software, and a JD for a remote or nearshore agentic AI engineer needs to say so. An agent that silently loops on a bad tool call at 2 a.m. does not throw a stack trace the way a crashed server does. Candidates who have actually shipped agents in production will notice if your JD hasn’t thought this through.

State the Timezone Requirement Precisely

“Full overlap with US Eastern business hours (9 am to 6 pm ET, UTC-5/UTC-4 with DST)” removes ambiguity that a vague “US timezone overlap” line leaves open. Nearshore hires from Colombia, Costa Rica, and Mexico give you 6 to 8 hours of real-time overlap with any US timezone, which matters more for this role than most, since debugging an agent’s reasoning chain over async messages is genuinely harder than debugging a stack trace over async messages.

Describe the Agent Incident Model, Not Just the On-Call Rotation

State what actually happens when an agent misbehaves in production. Does it get killed automatically past a cost or step-count threshold? Does a human review flagged sessions the next morning, or is there a live escalation path? “Agents auto-halt after 15 steps or $2 in token spend per session. Flagged sessions are reviewed within 4 business hours” tells a candidate more about your engineering maturity than any list of frameworks. If the engineer will be in a live incident rotation for agent failures, say so and describe the compensation model for after-hours response.

Be specific about onboarding access too. “First three weeks are sandbox-only, no production tool-calling permissions until the eval suite is reviewed with the team” signals that your organization understands what’s actually risky about handing an unproven agent real-world tool access. That kind of specificity attracts engineers who take agent safety seriously, which is exactly the trait you want in someone building systems that can take autonomous action.

Common JD Mistakes

Requiring Every Agent Framework at Once

Asking for “LangGraph, CrewAI, AutoGen, and OpenAI Agents SDK experience” in one requirements list tells strong candidates you haven’t picked a stack, or don’t understand that these frameworks solve overlapping problems differently. Pick the one your team runs, require depth there, and list the others as a nice-to-have if you’re still evaluating options.

Treating Agentic AI Engineering as a Data Science Role

This role is closer to distributed systems and backend engineering than to model training or data science, and a JD that emphasizes statistics, pandas, and Jupyter notebooks will attract the wrong candidates entirely. The hard problems here are reliability, tool integration, evaluation, and cost control under production load, not model architecture.

Skipping the Evaluation and Testing Requirement

A JD that never mentions evaluation, regression testing, or observability signals that your team hasn’t built the discipline yet, which is fine, but say so honestly rather than implying a mature eval culture that doesn’t exist. Candidates who’ve been burned by shipping agents without a real testing loop will ask about this directly in the interview regardless of what the JD says.

Ignoring Token Cost and Latency as Engineering Constraints

Agentic systems that chain multiple LLM calls can get expensive and slow fast, and engineers who’ve only worked with single-call completions often don’t anticipate this until it’s a production problem. If cost or latency budgets are part of the role’s real constraints, state them in the JD rather than letting the candidate discover them after their first sprint.

Frequently Asked Questions