Artificial Intelligence Engineer

AI Engineer (Mid-Senior)

Company: Solvedex

Location: LATAM (Remote)

About Solvedex

Solvedex is a fast-growing Talent-as-a-Service (TaaS) startup focused on delivering high-quality, flexible talent solutions. We specialize in a fractional talent model that enables organizations to access top-tier professionals without the constraints of traditional hiring. As a small but ambitious organization, we are building a scalable and modern approach to staffing and talent delivery, helping companies stay agile while accessing the expertise they need, when they need it.

Role Overview

We're looking for a Mid-Senior AI Engineer to design, build, and deploy AI-powered features and applications, from LLM-based products to production-grade machine learning pipelines. You'll work across the full lifecycle—from prototyping and model/API selection to evaluation, deployment, and monitoring—partnering closely with product and engineering teams to turn AI capabilities into reliable, scalable solutions for our clients.

Key Responsibilities

AI/ML Solution Design & Development

● Design, build, and deploy AI-powered features and applications, including LLM-based products (chatbots, copilots, agents, RAG systems).

● Develop and integrate solutions using LLM APIs (OpenAI, Anthropic, or similar) as well as open-source models.

● Build and maintain Retrieval-Augmented Generation (RAG) pipelines, including chunking, embeddings, and vector database integration.

● Design and implement prompt engineering strategies, fine-tuning, and evaluation frameworks to improve model performance and reliability.

Engineering & Productionization

● Write clean, well-tested, production-grade Python code for AI/ML services and pipelines.

● Build and maintain data pipelines for training, evaluation, and inference workflows.

● Deploy and monitor models and AI services in cloud environments (AWS, Azure, or GCP).

● Implement MLOps best practices: versioning, CI/CD for ML, experiment tracking, and observability.

● Optimize for latency, cost, and scalability in production AI systems.

Collaboration & Technical Ownership

● Partner with Product Managers and Engineers to translate business problems into AI/ML solutions.

● Evaluate and recommend the right models, frameworks, and tools for each use case (build vs. buy, open-source vs. API-based).

● Establish evaluation metrics and testing strategies to measure model quality, safety, and business impact.

● Document architecture, experiments, and decisions to support long-term maintainability.

● Stay current with the fast-evolving AI landscape and proactively bring new techniques and tools into the team.

Requirements

Must-Have

● 4–7 years of experience in Software Engineering, Machine Learning, or AI Engineering roles.

● Strong Python skills, with experience building production-grade applications and services.

● Hands-on experience building applications with LLMs (OpenAI, Anthropic, or similar), including prompt engineering and API integration.

● Practical experience with RAG architectures, embeddings, and vector databases (e.g., Pinecone, Weaviate, FAISS, pgvector).

● Solid understanding of machine learning fundamentals and experience with frameworks such as PyTorch or TensorFlow.

● Experience with cloud platforms (AWS, Azure, or GCP) for deploying and scaling AI/ML workloads.

● Familiarity with MLOps practices: CI/CD, model versioning, monitoring, and experiment tracking.

● Strong understanding of APIs, data pipelines, and system design for AI-driven products.

● Advanced English (C1), required for daily written and verbal communication with US-based, cross-cultural teams.

● Comfortable operating in fast-paced, ambiguous environments with evolving priorities.

Highly Valued

● Experience with agentic frameworks (LangChain, LlamaIndex, AutoGen, or similar).

● Experience fine-tuning or customizing open-source LLMs.

● Familiarity with containerization and orchestration tools (Docker, Kubernetes).

● Experience working in startup or client-facing consulting environments.

● Exposure to responsible AI practices: bias evaluation, safety guardrails, and data privacy considerations.