AI Engineer (P-180)

We are agents for tech professionals across Latin America who help them build careers with companies in the United States.

We believe in long-lasting relationships with our talent. We invest time getting to know them and understanding what they seek as their professional next step.

We aim to find the perfect match. As agents, we pair our talent with our US clients, not only by their technical skills but also by cultural fit. Our core competency is finding the right talent fast.

We purposefully move away from the traditional “outsourcing” relationship. Our clients are looking for professionals who become an integral part of their teams, and so are we.

• Wellness Coverage
• Remote Work
• Birthday day off
• Recognition and rewards system
• Referrals Program
• Business skill coaching
• English classes for Smashers and relatives
• Learning opportunities

This is a remote position supporting a US-based company. Applicants must be legally authorized to work in their country of residence within Latin America (LATAM).

We are looking for an experienced AI Engineer to design and build production-grade AI/ML applications for scalable SaaS products.

This is a hands-on engineering role focused on LLMs, AI agents, orchestration, ML model deployment, inference optimization, APIs, and scalable data infrastructure. You will work closely with backend engineering teams and global stakeholders across the US, Spain, and India to bring AI capabilities into production.

A critical part of this role is working directly with customers to understand their requirements and customize AI agents and orchestration layers for product deployments. Experience building SaaS products within regulated environments such as HealthTech or FinTech is particularly important.

• Design, build, deploy, and maintain production-grade AI/ML applications.
• Integrate machine learning and deep learning models into SaaS products and production environments.
• Design and implement APIs for serving ML and AI models.
• Build AI agents and orchestration layers supporting complex product workflows.
• Customize agentic workflows and orchestration based on specific customer requirements and deployment environments.
• Work directly with customers to understand technical requirements and translate them into scalable AI solutions.
• Fine-tune and optimize Large Language Models (LLMs) for production use cases.
• Optimize model inference for latency, throughput, scalability, and cost.
• Implement caching and other performance optimization strategies for AI workloads.
• Design and implement RAG and vector-search solutions where appropriate.
• Build and maintain scalable data pipelines supporting model training, inference, evaluation, and monitoring.
• Develop high-quality production code using Python and, where applicable, Go or Java.
• Build APIs and backend services using frameworks such as FastAPI and Flask.
• Develop distributed data processing workflows using PySpark.
• Work with frameworks and technologies such as PyTorch, TensorFlow, Hugging Face, LangChain, and LlamaIndex.
• Implement vector database solutions using technologies such as Pinecone, Qdrant, or pgvector.
• Build and maintain asynchronous processing workflows using tools such as Celery.
• Containerize AI applications and services using Docker.
• Deploy and operate AI workloads across AWS, GCP, or Microsoft Azure.
• Own testing, CI/CD, deployment, and production-readiness practices for AI services.
• Collaborate closely with backend engineers on system architecture, integrations, APIs, and production deployments.
• Design solutions capable of handling large-scale data and production workloads.
• Work effectively with distributed engineering and product teams across multiple countries and time zones.
• Ensure solutions align with the security, privacy, reliability, and compliance expectations of regulated SaaS environments.

• 3–4+ years of professional software engineering experience.
• 2+ years of hands-on experience building and deploying AI/ML solutions in production.
• Strong professional experience with Python.
• Hands-on experience with FastAPI and/or Flask.
• Experience with PySpark and large-scale data processing.
• Production experience with at least one major ML/DL framework such as PyTorch, TensorFlow, or Hugging Face.
• Hands-on experience building solutions using Large Language Models (LLMs).
• Demonstrable experience creating AI agents and orchestration layers.
• Experience with LLM frameworks such as LangChain and/or LlamaIndex.
• Experience with vector databases such as Pinecone, Qdrant, pgvector, or equivalent technologies.
• Experience fine-tuning and/or optimizing LLMs for production use.
• Experience optimizing inference performance, including latency and caching.
• Strong experience designing and consuming REST APIs.
• Experience building production-grade SaaS products.
• Demonstrable SaaS experience within regulated environments, particularly HealthTech and/or FinTech.
• Experience with at least one major cloud platform: AWS, GCP, or Microsoft Azure.
• Hands-on experience with Docker.
• Experience with asynchronous processing or task queues such as Celery.
• Experience designing and maintaining data pipelines for model training, inference, and monitoring.
• Strong understanding of system design and scalable distributed systems.
• Experience with CI/CD, automated testing, and production deployment practices.
• Proven experience collaborating with globally distributed teams.
• Strong customer-facing capabilities, including experience gathering requirements and adapting technical solutions to customer needs.
• Ability to independently own AI capabilities from design through production deployment.
• Fluent English communication skills.

• Professional experience with Go and/or Java.
• Experience building advanced RAG architectures.
• Experience with multi-agent systems and complex agent orchestration.
• Experience implementing AI evaluation, observability, and model monitoring frameworks.
• Experience with prompt management and systematic LLM evaluation.
• Experience optimizing AI infrastructure for cost and performance.
• Experience deploying AI services at significant production scale.
• Familiarity with security, privacy, data governance, and compliance requirements for AI applications.
• Experience working directly with enterprise customers during technical implementations.
• Experience across multiple cloud providers.

• English C1 or higher
• Spanish C1