AI Engineer (P-180)

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