Senior AI Engineer, Ops Labs

Félix

. Design, build, and scale autonomous AI agents for product and internal operations .

Posted 11/6/2026full-timeRemote • MexicoSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying production-grade AI agents, with a strong focus on LLMs and generative AI. Proficient in Python and experienced in integrating AI solutions into operational workflows while ensuring compliance and stability.

Highest-signal resume keywords

Python ProgrammingLLM API ExpertiseProduction AI Agent DevelopmentCI/CD DeploymentAsynchronous Programming

ATS Keywords

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Hard Skills

Software EngineeringAI/ML Application DevelopmentState ManagementError HandlingStructured Data ValidationPredictive TriggersDecision AutomationAutomated Evaluation PipelinesRAG Pipeline OptimizationHuman-in-the-Loop Controls

Soft Skills

MentoringCollaborationProblem-SolvingCommunication

Tools & Technologies

OpenAI SDKGoogle AI SDKCrewAILangChainGitGitHub

Industry Keywords

AI OperationsCustomer Experience (CX)ComplianceTransaction SystemsOperational Automation

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Design, build, and scale autonomous AI agents for product and internal operations
Develop end-to-end AI strategies and use cases to automate and scale CX operations
Partner with the Head of Ops Labs and Ops leadership to identify operational problems and scope AI opportunities
Design AI-powered workflows with predictive triggers, LLM categorization, decision automation, and alert systems
Architect, ship, and maintain stateful production AI agents that call tools, move cases between queues, and retain state across case lifecycles
Establish LLMOps monitoring, tracing, and observability for agentic workflows
Integrate AI agents into CX, compliance, hold-queue, and transaction systems with least-privilege access
Own containerization, CI/CD deployment, and post-launch stability
Build automated evaluation pipelines, including trajectory evaluations, safety checks, groundedness testing, and business outcome metrics
Optimize RAG pipelines and agent loops through caching, prompt compression, and model routing
Mentor junior engineers and drive best practices in asynchronous programming, error handling, and structured data validation
Design human-in-the-loop controls, confidence thresholds, escalation paths, approval gates, and audit trails
Partner with AI leads, engineering teams, and product managers to turn AI strategy into a roadmap

Requirements

What you’ll need

5+ years of hands-on experience in software engineering, with at least 2 years dedicated to building and deploying production-grade AI/ML applications focused on LLMs or generative AI
Mandatory expertise in Python
Deep familiarity with at least one core LLM API or framework, such as OpenAI SDK, Google AI SDK, CrewAI, or LangChain
Proven experience shipping production AI agents that take actions, including tool calling, multi-step orchestration, state management, human-in-the-loop escalation, guardrails, and failure handling
Strong understanding of software development best practices, Git/GitHub, and CI/CD pipelines
Ability to translate ambiguous operational briefs into testable hypotheses and validate assumptions through experiments
Ability to translate high-level business needs into concrete, maintainable, well-tested code
Experience building AI for CX, support, compliance, or other operations teams
Equivalent competencies in the listed areas will also be considered

Benefits

Comp & perks

Competitive salary
Initial stock options grant
Annual performance bonus
Unlimited PTO
Paid parental leave
Work model: currently fully remote, with a future transition to a hybrid setup
Health, dental, and vision plans
Continuous learning opportunities
Empowering opportunities for growth in a dynamic entrepreneurial environment