Job Description 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 #J-18808-Ljbffr