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SMASH, Who we are?

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. ##

Our Benefits

- 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).* ##

Role summary

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. ##

Responsibilities

• 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. ##

Requirements – Must-haves

-

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.

##

Nice-to-haves (optional)

• 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. ##

Languages

• English C1 or higher - Spanish C1