Location -
MedellÃn, 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:
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Build
production-ready AI systems using modern software engineering practices.
-
Integrate
AI capabilities into enterprise applications, APIs, and workflows.
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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