AI Engineer

Solvedex; Brasil; On-site; BRL 180,000 - 280,000; Full time

Job summary

Solvedex is seeking a Mid-Senior AI Engineer to design, build, and deploy AI-powered features and applications, from LLM-based products to production-grade ML pipelines. You will work across the full lifecycle—prototyping, model selection, evaluation, deployment, and monitoring—collaborating with product and engineering teams.

The role emphasizes prompt engineering, RAG architectures, vector databases, and MLOps practices, with production Python services on cloud platforms.

Qualifications

• 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.

Responsibilities

• AI/ML Solution Design & Development: Design, build, and deploy AI-powered features and applications, including LLM-based products (chatbots, copilots, agents, RAG systems).
• Engineering & Productionization: Write clean, well-tested, production-grade Python code for AI/ML services and pipelines; deploy and monitor models in cloud environments.
• Collaboration & Technical Ownership: Partner with Product Managers and Engineers to translate business problems into AI/ML solutions; establish evaluation metrics and documentation.

Skills

Python

LLMs

RAG

API integration

PyTorch

Cloud platforms

MLOps

Data pipelines

System design

English (C1)

Tools

Docker

Kubernetes

Pinecone

Weaviate

FAISS

pgvector

Job description

Solvedex reshapes the future of tech talent through fractional technology leadership. We provide experienced, pre-vetted leaders who integrate seamlessly to guide strategy, execution, and growth, handling alignment and oversight so organizations can stay focused on innovation

About the Role

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.

Note: Please share your CV in English; CVs in other languages will not be considered for the process.

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.

Additional Requirements

• Comfortable working remotely with minimal supervision
• Proactive, detail-oriented, and collaborative
• Ability to thrive in a fast-paced, startup-like environment.