Location - Medellín, Colombia | Hybrid / Onsite / Flexible

Role Overview

We are seeking a Senior AI Engineer (LLMs & Generative AI) to design, build, and scale enterprise-grade AI systems powered by large language models, generative AI technologies, and massive datasets. This role is ideal for a highly skilled engineer who has hands-on experience working with LLMs in production, understands how to implement AI guardrails and responsible AI practices, and has built scalable AI solutions across complex enterprise environments.

You will play a key role in shaping AI capabilities across the organization — from model orchestration and retrieval systems to safety, governance, and performance optimization — while collaborating with global teams in a fast-moving, innovation-driven environment. We are especially interested in bilingual (English/Spanish) professionals who can operate effectively across technical and business teams internationally.

Key Responsibilities

LLM & Generative AI Development:

• Design, develop, and deploy applications powered by LLMs and generative AI models.
• Build solutions for use cases such as enterprise search, document intelligence, summarization, conversational AI, and workflow automation.
• Work with both commercial and open-source LLMs depending on use case and performance requirements.
• Optimize prompts, model parameters, and inference workflows for quality, latency, and cost.

AI Guardrails & Responsible AI:

• Design and implement AI guardrails to ensure safe, reliable, and policy-aligned outputs.
• Develop mechanisms for hallucination mitigation, content filtering and moderation, prompt injection defense, output validation and verification, and access and usage controls.
• Build evaluation frameworks to measure accuracy, safety, groundedness, and consistency.
• Partner with security and compliance teams to align AI systems with enterprise governance standards.

Massive Data & RAG Systems:

• Work with large-scale structured and unstructured data to power AI systems.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines.
• Develop workflows for data ingestion and preprocessing, chunking and embedding, indexing and vector search, context retrieval and ranking.
• Collaborate with data engineering teams to ensure scalability and performance across large datasets.

Model Orchestration & Evaluation:

• Implement orchestration strategies across multiple models and APIs.
• Develop fallback, routing, and hybrid model strategies for optimal outcomes.
• Define and track evaluation metrics for model performance.
• Conduct benchmarking, A/B testing, and continuous improvement of AI systems.
• Engineering & Deployment:
• Build production-ready AI systems using modern software engineering practices.
• Integrate AI capabilities into enterprise applications, APIs, and workflows.
• Support CI/CD pipelines, versioning, testing, and monitoring of AI services.
• Ensure systems are scalable, observable, and cost-efficient.

Cross-Functional Collaboration:

• Partner with product, engineering, data, and business teams to translate requirements into AI solutions.
• Communicate technical concepts clearly to stakeholders across global teams.
• Contribute to architecture decisions, documentation, and reusable AI frameworks.
• Work effectively in English-speaking environments with international stakeholders.

Requirements

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, Data Science, or related field.
• 5+ years of experience in software engineering, AI, or machine learning roles.
• Strong hands-on experience building applications using LLMs and generative AI technologies.
• Experience working with large-scale enterprise data environments.
• Proven understanding of prompt engineering, RAG architectures, model evaluation, AI safety and guardrails.
• Strong programming skills in Python and experience with backend development.
• Ability to design and deploy production-grade AI systems.
• Fluent English communication skills (required).
• Bilingual (English/Spanish) preferred.

Preferred Technical Experience

• Experience with frameworks and tools such as LangChain, LlamaIndex, Semantic Kernel, Hugging Face ecosystem, OpenAI / Azure OpenAI / Anthropic APIs.
• Experience with vector databases (e.g., Pinecone, Weaviate, FAISS), embeddings and semantic search, model fine-tuning or adaptation techniques, and AI observability and monitoring tools.
• Familiarity with cloud platforms (AWS, Azure, or GCP), Databricks, Spark, or distributed data systems, Docker, Kubernetes, and scalable deployment patterns, MLOps pipelines and AI lifecycle management.
• Understanding of security, privacy, and compliance in AI systems, enterprise governance, and access controls.

Preferred Certifications

• Microsoft Certified: Azure AI Engineer Associate
• AWS Certified Machine Learning – Specialty
• Google Professional Machine Learning Engineer
• Databricks Machine Learning Certification
• TensorFlow Developer Certificate
• Certifications in Responsible AI, AI Governance, or Data Engineering

What We’re Looking For

• A hands-on AI engineer who can take solutions from concept to production
• Strong expertise in LLMs, generative AI, and enterprise-scale data systems
• Someone who understands that AI safety and guardrails are critical
• A professional who can balance innovation with reliability and governance
• A bilingual, globally-minded communicator who collaborates effectively across teams
• A candidate with strong ownership, curiosity, and problem-solving ability

Why This Role is Attractive

• Work on cutting-edge generative AI and LLM initiatives
• Solve real-world enterprise problems with high impact
• Exposure to large-scale data, modern AI architectures, and global teams
• Strong growth path in one of the most in-demand fields globally
• Opportunity to shape how AI is safely and effectively deployed at scale