AI Engineer
Panama City, Panama, PanamaAll locationsPanama City, Panama, PanamaBogotá, Columbia, ColombiaAsuncion, Parugauy, ParaguayBelize City, BElize, BelizeLima, Peru, PeruSanto Domingo, Dominican Republic, Dominican RepublicManagua, Nicaragua, NicaraguaBuenos Aires, Argentina, ArgentinaTegucigalpa, Honduras, HondurasMexico City, Mexico, Mexico Remote
$30,000–$36,000 a yearJobFig found this opening at its original source and checks that it remains available.
About the role
We are looking for a hands-on AI Engineer to design, build, and deploy intelligent systems across a range of client environments. This is a technically deep role suited to someone who thrives at the intersection of machine learning, software engineering, and product thinking. You will work across the full AI development lifecycle — from prototyping and model integration to production deployment and ongoing optimisation. The ideal candidate is fluent in modern AI tooling, thinks in systems, and can translate complex technical concepts into scalable, working solutions.
What you'll bring
- Strong programming skills in Python
- solid understanding of software engineering fundamentals
- Hands-on experience building with LLMs (OpenAI, Anthropic, Mistral, or similar) via API and SDK
- Practical experience with RAG architectures, vector databases (Pinecone, Weaviate, Chroma, etc.), and prompt engineering
- Familiarity with AI agent frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI
- Solid understanding of REST APIs and experience integrating third-party services and data sources
- Ability to work autonomously in a fast-paced remote environment with minimal hand-holding
- Applications without this experience will not be considered.
- Must have prior remote work experience, be fluent with remote collaboration tools and platforms (such as Slack, Zoom, Google Workspace, Asana, or similar), and have ideally worked with US or UK-based companies.
- Experience with model fine-tuning, RLHF, or custom training workflows
- Familiarity with MLOps tooling and model deployment pipelines (Docker, cloud functions, etc.)
- Exposure to multimodal systems (vision, audio, or document understanding)