Resume Example & Template

AI Agent Engineer Resume Example

Building agents that survive production — and a CV that shows you know the model was never the hard part.

What is a AI Agent Engineer?

An AI Agent Engineer builds systems where a model takes actions rather than only producing text: calling tools, working through multi-step tasks, recovering from failures and knowing when to stop or hand over. The interesting engineering is almost never the prompt. It is tool design, idempotency, retry policy, observability and deciding which actions a machine should never take unsupervised.

The role emerged as teams discovered that a demo agent and a production agent are different products. Demos succeed on the happy path; production means the third-party API times out, the model picks the wrong tool, and something has to be safe to retry. Most of the work is engineering discipline applied to a non-deterministic component.

Job titles are still settling — AI Agent Engineer, Agentic Systems Engineer, LLM Application Engineer, AI Engineer — but the interview is consistent: what broke in production, and what you changed as a result.

Key skills for a AI Agent Engineer resume

agent orchestration
tool use
function calling
multi-step planning
failure recovery
human-in-the-loop
guardrails
idempotency
observability
tracing
LLM APIs
RAG
context management
structured outputs
Python

AI Agent Engineer resume example

Nikhil Sharma

AI Agent Engineer

Amsterdam, Netherlands

Summary

Engineer building agentic systems that survive contact with reality. Shipped a multi-step agent handling 12,000 tasks a day in production, where the hard problems were tool design, failure recovery and knowing when to stop. Raised task completion from 61% to 89%.

Experience

AI Agent Engineer · Kestrel Logistics Tech

Jan 2024 – Present

Built and run a production agent resolving shipment exceptions end to end — 12,000 tasks daily with human handoff on low confidence.
Raised task completion from 61% to 89%, almost entirely through tool design, retry policy and explicit stopping conditions rather than model changes.
Designed the tool layer — idempotent, narrowly scoped, individually testable — so a failed step retries without side effects.
Built the trace and replay tooling the team uses to debug runs; every production failure is reproducible locally.

Backend Engineer · Kestrel Logistics Tech

Aug 2021 – Dec 2023

Built the internal API platform the agent layer now calls, including auth, rate limiting and observability.

Education

MSc Computer Science

Delft University of Technology · 2017 – 2019

BE Computer Engineering

Savitribai Phule Pune University · 2013 – 2017

Certifications

Skills

Agentic Systems: Agent Orchestration · Tool Use · Failure Recovery · Human-in-the-Loop · Guardrails

LLM Engineering: LLM APIs · RAG · Context Management · Structured Outputs · Evaluation

Backend: Python · FastAPI · PostgreSQL · Redis · Idempotency

Use this as your starting point

How to write a AI Agent Engineer resume that stands out

Lead with a production number. "12,000 tasks a day at 89% completion" instantly separates you from candidates whose agents only ever ran in a notebook.
Attribute the improvement honestly. "Raised completion from 61% to 89% through tool design and retry policy, not model changes" is the sentence that shows real experience.
Show your tool design thinking — narrow scope, idempotent, individually testable. Reviewers care far more about this than about which framework you used.
Name the guardrails on irreversible actions. Any engineer who has run an agent near money or customer communication has an opinion here, and its absence is noticed.
Include the debugging story. Trace and replay tooling is what makes agent work sustainable, and building it signals you have supported one beyond launch.
Be specific about human handover — the confidence threshold, what gets escalated, who reviews it. "Fully autonomous" reads as inexperience to anyone who has operated one.

AI Agent Engineer resume — FAQ

How is this different from an AI or ML Engineer role?

An ML engineer is usually closer to models and data. An agent engineer is closer to systems: APIs, state, retries, failure handling. If you have strong backend experience and good LLM fluency, this is often the more natural fit.

Which frameworks should I list?

List what you actually used, but do not build the CV around it. Frameworks in this space change every few months; tool design, failure handling and evaluation transfer across all of them and are what interviewers probe.

What if my agent work has only been prototypes?

Say so plainly and lead with what you learned about failure. A candidate who can articulate why their prototype broke often interviews better than one who claims production experience they cannot describe in detail.

How do I show reliability work on a CV?

Through numbers and mechanisms: completion rate, retry policy, idempotent tools, human handover thresholds, trace tooling. Those five specifics tell an experienced reviewer everything they need.

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