Senior AI Engineer, Ops Labs

Felix is hiring a Senior AI Engineer, Ops Labs. Level rates it ; you can apply on Felix.

Level's read on this job

On Level's scale, this AI engineer role is Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include OpenAI, LangChain and LangGraph.
Level classifies it as a senior role.
Level lists 105 other open roles at Felix.
The role was posted in the last few days.

AI in this role

openailangchainlanggraphcrewaiweights-and-biases

ragai-agents

About Us

At Félix, we are building the indispensable financial companion for Latinos in the US. We combine an AI-powered, conversational-first interface with real-time financial infrastructure to make cross-border money movement as easy as sending a text. Starting with fast, affordable remittances powered by AI and crypto rails, we are expanding into credit, savings, and wallet services to support the complete immigrant financial journey. Our ambition is to deliver a white-glove financial experience with the simplicity of a conversation to a community the traditional financial system has historically overlooked.
We are a hyper-growth Series C company, backed by over $300 million in funding from top-tier global investors, including Andreessen Horowitz, QED, Castle Island, Switch Ventures, HTwenty, Monashees, General Catalyst Customer Value Fund. This isn't just about the numbers; it's a testament to the trust our investors have in our vision and our team. Additionally, Félix was selected as an “Endeavor Entrepreneur” and was a recipient of the CrossTech Fintech Startups Award.
Joining Félix means you will be part of a team building a legacy, a company that will outlive us all. This is a rare opportunity to apply your skills to a deeply meaningful mission—serving a community that has been underserved for too long because we are obsessed with our customers. We get things done with urgency and focus, driven by extreme ownership over our impact. We collaborate without ego, fostering radical transparency and fierce loyalty so we can grow together. Because we aim for insanely great rather than just good enough, we stay insatiably curious—always experimenting, building the future today, and delivering a product that truly makes our users' lives better.

About the Role

We are seeking a proactive and highly skilled AI Engineer to join our core AI team to work solely on Ops Labs. This role is central to Félix's next phase of growth, focusing on designing, building, and scaling autonomous AI Agents that will integrate directly into our product and internal operations. We are building a world-class AI team and believe great talent comes in various shapes. We are open to hiring at the Semi-Senior, Senior, or Staff level. We will calibrate your role, title, and compensation package based on your technical depth, architectural ownership, and the scope of impact you've historically delivered.
You will be instrumental in evolving our internal AI ecosystem, leveraging the productivity and impact in the CX Operations team, by designing and developing the end-to-end strategy and use cases that rely on AI to automate and scale our operations. You will work particularly close with the Head of Ops Labs to surface and scope AI opportunities across the full system, address operational problems, design AI-powered solutions that automate while giving CX Ops agents visibility and authority, and validate impact before scaling. You'll learn the agent workflow and the managerial landscape, to map both onto technical feasibility. Your work will directly enable CX Ops agents to strengthen customer relationships. You'll balance technical elegance with operational constraints and help design evaluation frameworks that measure business outcomes alongside technical quality. Also, you will partner closely with AI leads, as well as with other engineering teams and PMs to translate our AI strategy into a tangible roadmap and bring it to life.
This is a unique opportunity to apply a bias toward action and an experimentation spirit to solve deeply meaningful, real-world problems for Latin immigrants in the U.S. If you thrive in a rapid-iteration environment and want to build the infrastructure that powers a hyper-growth fintech, this is your mission.

What You’ll Own

Ops-Integrated AI Solutions: Partner with Ops leadership to transform operational friction into AI-assisted workflows. Design end-to-end solutions that span: predictive triggers, LLM-powered categorization, decision automation, and alert systems.
Production-Grade Agent Architecture: Architect, ship, and maintain stateful AI agents that take real actions in ops workflows: calling tools, moving cases between queues, and carrying state across a case’s full lifecycle. You’ll use Python and modern frameworks (Google ADK, LangGraph, CrewAI, etc.), and the bar is shipped AI agents that handle real edge cases.
LLMOps & Observability: Establish production monitoring for agentic workflows. Implement tracing and observability (e.g., LangSmith, Arize Phoenix, or Weights & Biases) to track reasoning paths, tool-calling success rates, and latency bottlenecks.
End-to-End Production Integration: Lead the integration of AI agents into ops systems (CX platform, compliance and hold queues, transaction tooling) with least-privilege access. Own the full lifecycle, from containerization (Docker/Kubernetes) to CI/CD deployment and post-launch stability.
Advanced Evaluation Pipelines: Move beyond basic metrics. Design automated "evals-as-code" using LLM-as-a-judge, semantic similarity testing, and adversarial benchmarking to ensure agent safety and groundedness before every release. This includes trajectory evals that check whether an AI agent called the right tools in the right order, plus business metrics like resolution time, escalation rate, and CX agent override rate.
Performance & Cost Engineering: Optimize RAG pipelines and agent loops for production constraints. Implement caching strategies, prompt compression, and model routing to balance inference costs with high-performance requirements.
Technical Leadership: Mentor junior engineers on software craft. Drive best practices in asynchronous programming, error handling for non-deterministic outputs, and structured data validation (Pydantic, etc.).
Human-in-the-Loop & Autonomy Design: Define what each AI agent does on its own, what it only recommends, and what it never touches. Build confidence thresholds, escalation to CX agents, approval gates for irreversible actions, and audit trails for every decision.

Requirements

Experience: 5+ years of hands-on experience in software engineering, with at least 2 years dedicated to building and deploying production-grade AI/ML applications, specifically focused on Large Language Models (LLMs) or generative AI.
Technical Mastery: Mandatory expertise in Python and deep familiarity with at least one core LLM API or framework (OpenAI SDK, Google AI SDK, CrewAI, LangChain, etc.).
Agentic System Knowledge: Proven experience shipping AI agents to production that take actions, not just answer questions. This includes tool calling, multi-step orchestration, state management, human-in-the-loop escalation, guardrails, and failure handling (fallbacks, rollback). RAG and vector databases are a plus.
Engineering Fundamentals: Strong understanding of software development best practices, version control (Git/GitHub), and CI/CD pipelines. Experience with personal projects or demonstrable contributions on GitHub is a strong plus.
Operational Rigor: Demonstrated ability to translate ambiguous operational briefs into testable hypotheses. Comfortable with incomplete specs, can ask clarifying questions about success metrics, and understand trade-offs between technical sophistication and operational constraints. Experience validating assumptions via experiments.
Execution & Communication: Proven ability to translate high-level business needs into concrete, maintainable, and well-tested code.
Ops Experience: Experience building AI for CX, support, compliance, or other operations teams. Familiarity with ops metrics like AHT, FCR, CSAT, and SLA is a strong plus.
These are the applicable requisites, although equivalent competencies in any of the above will also be considered.

What We Offer

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

Equal Opportunity Employer

At Félix, we are committed to providing equal employment opportunities to all qualified employees and applicants without regard to race, religion, nationality, sex, sexual orientation, gender identity, age, or disability. This policy applies to all terms and conditions of employment, including recruitment, hiring, placement, promotion, training, compensation, benefits, and termination.
Want to learn more about our privacy practices? Check out our Privacy Policy.

How we rate this

Senior AI Engineer, Ops Labs at Felix rates 92 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior AI Engineer, Ops Labs is an open job at Felix.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Argentina.
The job was posted on November 3, 2026.
Seniority: senior.
Applications are handled by Felix. Apply on Felix

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGAI agentsOpenAILangChainLangGraphCrewAIWeights And Biases

Also common in AI engineer jobs at this level

Machine learning AWS Azure Google Cloud PyTorch MLOps

Pay

Typical AI engineer job: $183k

This posting doesn't list pay. Use the typical AI engineer figure when pay comes up. See AI engineer jobs

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How do you decide when an AI agent can act on its own versus asking for approval first?
What are the limits of OpenAI that you've run into, and how did you work around them?
What's a project where you used LangChain hands-on?
Walk me through how you've used LangGraph in your day-to-day work.

Adapt your resume

List these exact terms on your resume: RAG, AI agents, OpenAI, LangChain, and LangGraph. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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