AI agent developer job description: role, skills, template and 2026 pay data

What does an AI agent developer do?

An AI agent developer designs and ships systems where a language model plans and executes multi-step tasks by calling tools (APIs, databases, browsers, internal services), with limits, approvals and evaluation around it. The job sits between backend engineering and applied LLM work: most days go on tool contracts, state, scenario tests, traces and cost, not prompts alone.

It differs from an LLM application developer, who builds single-turn features such as RAG answers, and from a machine learning engineer, who trains and serves models. The agent developer consumes models through APIs and owns what happens between the model and the real world.

What responsibilities belong in the job description?

Pick the eight that match your agent; keep the verbs concrete.

• Design agents that complete defined business tasks end to end, from intake to a verified result or an escalation.
• Write tool definitions with typed schemas, clear descriptions and explicit error returns, and test each tool outside the agent.
• Implement the orchestration loop (planning, ReAct-style steps, retries, termination) using LangGraph, the OpenAI Agents SDK or an in-house runtime.
• Integrate external systems through MCP servers or direct APIs, with scoped credentials per tool.
• Build and maintain a scenario set and run trajectory evaluations (task success rate, tool-call correctness) on every change.
• Add guardrails: step and cost caps, idempotent actions, human approval for irreversible steps, and defences against prompt injection via tool results.
• Instrument traces and dashboards for latency, token cost per task and failure modes, and use them to fix incidents.
• Version prompts, schemas and model choices, ship behind flags and roll back safely.
• Write design docs and runbooks so product and support teams can operate the agent.

Which skills are required, and which are nice to have?

Required skills

• Strong Python or TypeScript and production backend experience.
• Tool calling and structured outputs with at least one major model provider.
• One agent framework in production: LangGraph, the OpenAI Agents SDK and Responses API, or a hand-built loop.
• Evaluation practice: building scenario sets, trajectory evals, LLM-as-judge with human checks.
• Guardrail design: limits, idempotency, approval gates, treating tool results as untrusted input.
• Observability: tracing every model and tool call, measuring cost and latency per completed task.
• State and memory design, including checkpointing and resumable runs.
• Clear written communication; agent design is mostly specification writing.

Nice to have

• MCP server authoring and operating a shared tool layer across several agents.
• RAG and vector search (pgvector, Qdrant) for agents that act over documents.
• Self-hosted inference (vLLM) or fine-tuning (LoRA, QLoRA) for cost-sensitive steps.
• Workflow tooling such as n8n, Zapier or Make for the deterministic parts of a process.

AI agent developer job description template (copy and edit)

Replace the bracketed parts and keep the posting under 600 words.

About the role

We are building [an agent that handles X for Y users, for example: triages inbound support tickets and resolves refunds under a limit]. You will own it from tool design to production: the loop, the guardrails, the evaluation set and the traces. You will work with [product owner] and [backend team] and report to [role].

Responsibilities

• Design and ship agents that complete [task] with measurable success rates.
• Define tools with typed schemas and explicit errors; integrate [systems] via MCP or APIs.
• Build the scenario set and trajectory evaluations; run them on every change.
• Implement limits, approvals, idempotent actions and prompt-injection defences.
• Instrument traces, track cost per task, and own incidents for your agent.

Requirements

• [3+] years of backend engineering in Python or TypeScript.
• At least one LLM agent shipped to real users, with tools and evaluation you can describe.
• Hands-on experience with [LangGraph / the OpenAI Agents SDK / an in-house runtime].
• Experience with tracing and cost control for LLM workloads.
• Clear written design and incident communication.

Nice to have

• MCP server development; RAG and vector search; vLLM or fine-tuning; [domain] experience.

How we work

[Remote, core hours HH to HH in TZ]. [Contract or full-time]; [pay range]. We review design docs before code, run evaluations in CI, and pay for take-home work. Interview loop: a screen, a technical hour, a design hour and a short production round, inside one week.

How does the role change by seniority?

• Junior (0 to 2 years). Builds and tests individual tools, extends scenario sets and fixes traced bugs inside a loop someone else designed. Needs strong backend basics.
• Mid (2 to 5 years). Owns one agent end to end: loop, state, guardrails, evaluation and traces, with review on anything that acts irreversibly.
• Senior (5+ years). Decides when an agent is the wrong answer, sets limits and approval policy, designs the evaluation strategy, handles incidents and model upgrades, and reviews others' tool contracts.
• Lead or staff. Runs the shared agent platform: the MCP tool layer, tracing standards, cost governance and security review across teams.

What do AI agent developers earn in 2026?

Few salary sources track "AI agent developer" as a title, so use adjacent titles and state a range. For full-time US roles, Indeed (updated 12 October 2026) reports an average base salary of $151,505 for AI developers, with a range of $92,136 to $249,131 across 3.8k job postings. Levels.fyi (updated 17 October 2026) puts the median total compensation for machine learning engineers at $280,000, with the 25th percentile at $200,000 and the 75th at $385,000; this skews to large technology companies. By level, KORE1's AI engineer salary guide (updated 5 August 2026) lists base pay of $90,000 to $135,000 at entry level, $140,000 to $210,000 at mid level and $180,000 to $280,000 at senior level, with LLM and generative AI specialists at $165,000 to $230,000 base for mid level.

For contractors, Arc's hire AI developers page (October 2026) says freelance AI developers on its platform typically charge $60 to $100 plus per hour, with engineers in Eastern Europe or Latin America around $75 to $95 per hour. Hyparz publishes indicative hourly bands for AI engineers that track the rates Arc.dev publishes; the quote depends on specialism, seniority and engagement length. Full cost modelling is in cost to hire AI engineers.

What are the common mistakes in AI agent developer job descriptions?

• Asking for ML research credentials (PhD, model training, papers) for a role that consumes models through APIs.
• Hiding the use case. "Build autonomous agents" attracts demo builders; "resolve refunds under $200 at a 95 percent success rate" attracts people who have shipped.
• Listing every framework. One shipped agent in any framework transfers; ten names signal nobody scoped the job.
• Omitting evaluation, guardrails and security. If the posting never mentions a scenario set or prompt injection, you will interview people who have never built one.
• No pay range and no engagement model; this loses senior candidates before the first call.
• Calling the role "prompt engineer" when the work is backend engineering with a model in the loop.

Where to post it, and how to hire faster

Post on your careers page and the two or three boards where backend engineers in your region look, and share it with engineers who have shipped agents; their referrals are the highest-yield channel. Run the loop in the companion guide, AI agent developer interview questions, so that every candidate is scored on the same evidence.

To skip sourcing, Hyparz supplies pre-vetted remote AI agent developers as individual experts, blended teams or full remote teams. Applicants are screened for real engineering ability and tested on a practical agent task; about 5% of AI-role applicants pass vetting. Matches typically arrive within days, with a one-week risk-free trial (you keep any work delivered), a dedicated account manager, automated timesheets and source-code protection. Start at hire AI agent developers, or have the build delivered through AI agent development services.