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AI Developer
Free template · 2026

AI Developer Job Description Template

A ready-to-use AI Developer job description for 2026, with the interview scorecard and questions to hire one. Copy it as is or customize it free in about 15 seconds.

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Includes the job description, an interview scorecard and questions for every round.

Mid-level in Latin America
$5,500/mo
Same role in the US
$12,500/mo
You save
Up to 56%

All-in monthly cost, 2026 rates.

Job description template
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AI Developer

Build and improve AI features and automation that integrate with our products and data systems. You will take well-defined problems from design through deployment, monitor performance in production, and iterate based on real-world feedback.

Responsibilities

  • Design and implement AI-powered components (e.g., retrieval, ranking, classification, and agentic workflows) in production systems.
  • Integrate LLM and ML services with our applications and data pipelines using reliable APIs and service interfaces.
  • Create and maintain evaluation plans for model behavior, quality, and safety using quantitative metrics and targeted test suites.
  • Tune prompts, retrieval strategies, and model parameters to improve accuracy, latency, and cost while meeting quality requirements.
  • Collaborate with engineers and product stakeholders to translate user needs into technical approaches and measurable success criteria.
  • Set up monitoring and alerting for AI quality signals (e.g., drift, hallucination indicators, and error rates) and respond to incidents.
  • Document architectures, model/agent behavior, and operational runbooks so changes can be safely maintained over time.

Must-have qualifications

  • Hands-on experience building AI features with LLMs and/or modern ML in production systems
  • Strong prompt engineering and/or retrieval-augmented generation (RAG) experience with measurable evaluation
  • Experience designing evaluation approaches for model quality (offline and/or online) using appropriate metrics
  • Ability to implement reliable integrations with APIs, backends, and data sources for AI workflows
  • Working knowledge of safety and guardrails for AI systems (e.g., input/output filtering, policy enforcement, and refusal handling)

Nice to have

  • Experience with vector databases or retrieval indexing and relevance optimization in production
  • Familiarity with fine-tuning or model adaptation workflows (where applicable)
  • Experience deploying AI services with containerization and CI/CD best practices
  • Experience with incident response for model/agent regressions in production

What success looks like in 12 months

  • AI feature quality meets agreed targets
  • Evaluation coverage increases for critical behaviors
  • Reduce AI-related production issues
  • Latency and cost performance improvements
  • Robust safety and guardrails in production

Logistics

  • Full-time (40 hours a week), remote from Latin America.
  • Working hours overlap with US business hours.

About the company

[About your company: what you build, your stage and team size]

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Interview scorecard

Score every candidate against the same bar

Deliver reliable, measurable AI functionality in production by building, evaluating, and operating LLM/ML components that meet product and safety requirements.

Outcomes to measure

What this hire should deliver in the first year. Replace [N] with your own targets.

  • AI feature quality meets agreed targets. Measure: Improve model/e2e quality score(s) to [QUALITY_TARGET] and maintain them over [TIME_PERIOD] in production
  • Evaluation coverage increases for critical behaviors. Measure: Create or expand evaluation suite covering [N] critical scenarios with pass/fail thresholds aligned to [QUALITY_TARGET]
  • Reduce AI-related production issues. Measure: Reduce AI-caused incidents/regressions by [INCIDENT_REDUCTION]% within [TIME_PERIOD]
  • Latency and cost performance improvements. Measure: Improve p[95] latency by [LATENCY_IMPROVEMENT]% and/or reduce average inference cost by [COST_REDUCTION]% while keeping quality within [QUALITY_TOLERANCE]
  • Robust safety and guardrails in production. Measure: Achieve [SAFETY_REQUIREMENT]% compliance on safety/policy tests across [N] categories and maintain after [TIME_PERIOD]

Competencies and weights

What to evaluate, how much it counts and what a strong answer looks like.

LLM in Production · 22%
Core to delivering working AI features that integrate with real systems and behave consistently. Evaluated in Round 1.

  • Weak: Builds AI prototypes that run locally but lacks production-grade integration patterns
  • Meets the bar: Implements LLM/AI features that are deployed, monitored, and handle failures gracefully
  • Strong: Delivers production AI features with resilient service design, fallbacks, and clear operational ownership

Evaluation Design · 20%
Measurable evaluation is essential to ensure quality, regression prevention, and safe iteration. Evaluated in Round 2.

  • Weak: Uses vague quality checks without metrics or thresholds
  • Meets the bar: Defines evaluation metrics, test cases, and acceptance thresholds tied to product requirements
  • Strong: Builds robust evaluation systems (offline/online) that reliably predict regressions and guide iteration

RAG and Prompting · 18%
This role must improve answers using effective retrieval and prompt strategies grounded in measurable outcomes. Evaluated in Round 1.

  • Weak: Uses prompts without a clear evaluation plan or relies on single-pass generation
  • Meets the bar: Implements prompt and retrieval changes with documented rationale and observable quality impact
  • Strong: Iterates on prompts/retrieval using targeted tests to improve specific failure modes under constraints (latency/cost)

API Integration Reliability · 12%
AI workflows often fail at integration boundaries; reliability is key for production success. Evaluated in Round 2.

  • Weak: Connects components but does not address timeouts, retries, idempotency, or error handling
  • Meets the bar: Implements stable API calls and error handling patterns for AI workflows
  • Strong: Designs integration flows with defensive programming, observability, and graceful degradation

AI Safety Guardrails · 10%
Safety and policy enforcement must be implemented, tested, and operated—not assumed. Evaluated in Round 3.

  • Weak: Applies basic checks inconsistently or lacks test coverage for policy behavior
  • Meets the bar: Implements guardrails (input/output handling) and tests refusal/policy behaviors against criteria
  • Strong: Builds layered safety controls with measurable compliance and clear failure handling

Debugging and Monitoring · 10%
Mid-level independence requires diagnosing issues quickly and improving system behavior through monitoring. Evaluated in Round 3.

  • Weak: Responds to issues ad hoc without root-cause analysis or lasting fixes
  • Meets the bar: Investigates AI failures using logs/metrics, identifies root cause, and implements preventative improvements
  • Strong: Proactively uses monitoring signals to detect quality regressions early and drives durable remediation

Independent Delivery · 8%
The role works independently on well-defined work; execution quality and communication matter. Evaluated in Round 2.

  • Weak: Needs frequent guidance to complete well-defined tasks
  • Meets the bar: Plans and completes defined work items with clear status updates and risk awareness
  • Strong: Leads through ambiguity: proposes options, clarifies requirements early, and delivers reliably with minimal oversight
Interview questions

Questions for every interview round

Each round has an owner, a time box and questions that test the competencies above.

Round 1: AI Engineering Foundations
Senior AI Engineer · 45 min

  1. Tell us about a production LLM/AI feature you built end-to-end. What were the key integration points and how did you ensure it stayed reliable after release?
  2. Describe a RAG system or prompting approach you implemented. How did you decide what to retrieve, how did you structure prompts, and what measurable results did you observe?
  3. Walk through a failure mode you encountered (e.g., incorrect retrieval, hallucination, or formatting issues). What did you change and how did you validate the improvement?

Round 2: Evaluation and Integration
Staff Software Engineer · 55 min

  1. Explain how you design an evaluation plan for an AI feature. Which metrics and test cases do you use, and how do you set thresholds for passing vs failing changes?
  2. Share an example where an evaluation prevented a regression. What signal failed, how did you trace it, and what permanent fix did you implement?
  3. Describe how you structured API/data integration for an AI workflow (timeouts, retries, error handling, idempotency, and fallbacks). How did you instrument it to make debugging practical?
  4. Give an example of well-defined work you delivered independently. What risks did you identify early, and how did you communicate progress and tradeoffs?

Round 3: Safety and Operations
AI Product/Trust & Safety Lead · 50 min

  1. Discuss the safety/guardrails you implemented for an AI system. How did you handle policy enforcement (including refusal) and how did you test compliance?
  2. Tell us about a production incident or near-miss involving model/agent behavior. What monitoring signals did you use, what was the root cause, and what steps ensured the issue was less likely to recur?
  3. When quality degrades over time, how do you detect and respond (e.g., drift, changing inputs, retrieval issues)? Describe a concrete process you’ve used.

Red flags to watch for

  • Vague answers about production reliability (no discussion of monitoring, error handling, retries, or graceful degradation)
  • No clear evaluation approach or inability to name metrics/thresholds used to judge AI quality and regressions
  • Weak or inconsistent discussion of AI safety/guardrails and how they were tested
  • Claims of independent delivery without concrete examples of scoping, tradeoffs, and risk management

How much does it cost to hire an AI Developer in Latin America?

In 2026, a senior AI Developer in Latin America costs $8,250 to $9,500 a month all-in, versus $16,750 in the US: up to 51% less. Mid-level starts at $5,500.

Country

Mid-level

Senior

Argentina

$5,500

$8,250

Brazil

$6,125

$9,125

Colombia

$5,500

$8,250

Mexico

$6,375

$9,500

United States

$12,500

$16,750

Monthly cost in USD, all-in: the talent's salary plus Teilur's flat 20% fee, from our 2026 rates. The United States row shows what the same role typically costs there.

Compare costs in the calculator →

Frequently asked questions

What does an AI Developer do?

Typical responsibilities include: design and implement AI-powered components (e.g., retrieval, ranking, classification, and agentic workflows) in production systems; integrate LLM and ML services with our applications and data pipelines using reliable APIs and service interfaces; create and maintain evaluation plans for model behavior, quality, and safety using quantitative metrics and targeted test suites. The full list is in the template above.

How much does it cost to hire an AI Developer in Latin America?

In 2026, a senior AI Developer in Latin America costs $8,250 to $9,500 a month all-in, versus $16,750 in the US: up to 51% less. Mid-level starts at $5,500.

Can I customize this AI Developer job description?

Yes. Teilur's free Job Description & Scorecard Generator rewrites it for your stack, seniority, time zone and hiring urgency in about 15 seconds. No sign-up needed.

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