Artificial Intelligence Engineer

Solvedex; Colombia; Presencial; COP 120.000.000 - 180.000.000; Jornada completa

Descripción de la vacante

Solvedex is hiring an AI/ML Engineer to design and build scalable AI features for a fast-growing Talent-as-a-Service startup. You’ll work on LLM-based products, RAG pipelines, and production-grade Python services, collaborating with product managers and engineers.

You will deploy models on cloud platforms (AWS/GCP/Azure), implement MLOps, and optimize for latency, cost, and reliability while advancing AI capabilities for client projects.

Formación

• 4–7 years of experience in Software Engineering, Machine Learning, or AI Engineering roles.
• Strong Python skills and production-grade applications.
• Hands-on experience with LLMs (OpenAI, Anthropic, or similar) and API integration.
• Experience with RAG architectures, embeddings, and vector databases (e.g., Pinecone, Weaviate, FAISS, pgvector).
• Solid understanding of ML fundamentals and frameworks such as PyTorch or TensorFlow.
• Experience with cloud platforms (AWS, Azure, or GCP).
• Familiarity with MLOps: CI/CD, model versioning, monitoring, experiments.
• Strong APIs, data pipelines, and system design for AI-driven products.
• Advanced English (C1) for cross-cultural collaboration.
• Ability to operate in fast-paced, evolving environments.

Responsabilidades

• 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) and 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.
• Write clean, production-grade Python code for AI/ML services and pipelines.
• 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 latency, cost, and scalability in production AI systems.
• Collaborate with product managers and engineers to translate business problems into AI/ML solutions.
• Evaluate models and tools for each use case (build vs. buy, open-source vs. API-based).
• Document architecture, experiments, and decisions for long-term maintainability.
• Stay current with AI landscape and bring new techniques to the team.

Conocimientos

Python

LLMs

RAG

Embeddings

Vector databases

PyTorch

TensorFlow

Cloud platforms

MLOps

APIs

Data pipelines

System design

Advanced English (C1)

Ambiguity tolerance

Herramientas

Pinecone

Weaviate

FAISS

pgvector

LangChain

LlamaIndex

AutoGen

Docker

Kubernetes

Descripción del empleo

Role Overview

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.

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.