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Project Brief
We are seeking a high-caliber AI Specialist to build and scale advanced machine learning systems.
You will be responsible for developing state-of-the-art models using Deep Learning and Transformer architectures while ensuring absolute reproducibility through expert-level MLOps.
This role is at the intersection of rigorous mathematics and elite engineering, focusing on creating elegant solutions to complex AI problems.
Project Brief
We are seeking a high-caliber AI Specialist to build and scale advanced machine learning systems.
You will be responsible for developing state-of-the-art models using Deep Learning and Transformer architectures while ensuring absolute reproducibility through expert-level MLOps.
This role is at the intersection of rigorous mathematics and elite engineering, focusing on creating elegant solutions to complex AI problems.
Location:
Remote LATAM
Must-Haves
Machine Learning & Frameworks:
Strong proficiency in PyTorch or TensorFlow/Keras and hands-on experience with the Hugging Face ecosystem (Transformers, Datasets, Accelerate).
MLflow Mastery:
Expert knowledge of MLflow Tracking, Projects, and Models for full lifecycle management.
Engineering Excellence:
Expert-level Python (OOP, asynchronous programming) and advanced Git workflows including DVC.
Core Mathematics:
Deep theoretical understanding of the Attention Mechanism, Gradient Descent, and Linear Algebra.
Deployment & Tooling:
Proficiency with Docker and building/securing REST/gRPC APIs for model serving.
Nice to Have
Generative AI:
Familiarity with RAG architectures and vector databases such as Pinecone, Milvus, or Weaviate.
Advanced MLOps:
Experience with MLflow Recipes for standardized production workflows and basic Kubernetes orchestration.
Efficiency & Scale:
Experience with DeepSpeed, ONNX Runtime, or NVIDIA TensorRT, and knowledge of distributed training (Ray, Horovod, or DDP).
Cloud Platforms:
Hands-on experience with AWS SageMaker, GCP Vertex AI,



or Azure ML.
What You Will Do
Model Development:
Design and implement Deep Learning models and Transformers, using classical ML (Scikit-learn, XGBoost) for baseline comparisons.
Standardize Workflows:
Lead the implementation of reproducible ML environments using MLflow and Data Version Control (DVC).
Production Engineering:
Build, profile, and secure high-performance APIs to serve models in production environments.
Cross-Functional Delivery:
Work within 1-week sprints to move fast from research to delivery, ensuring robust and scalable AI solutions.
Why This Could Be Your Next Big Move
Work on Cutting-Edge Tech at Scale
- This is a unique opportunity to join an elite digital product agency where you will apply deep mathematical concepts to real-world products.
You will have high influence over the AI architecture and MLOps standards, working in a fast-paced environment that prioritizes technical excellence and elegant engineering.
If you are looking to bridge the gap between research-level AI and production-grade delivery, this is the place to do it.
Benefits & Compensation
Fully Remote
- work from anywhere in Latin America.
Long-term contract
- starting with a 6-month contract, then full-time.
Paid PTO
- details provided per role.
Referral Program
- earn a bonus for referring talent that gets hired.
To Apply
Please send your resume in
English
.
LinkedIn Profile URL
(Required).
GitHub Repository
- mandatory for this engineering-heavy AI role (Required).
Cover Letter
(Optional but encouraged) - tell us about the most complex Attention-based model you have deployed to production.
About OneSeven Tech
Founded by
James Sullivan
, OneSeven Tech is a premier digital product agency serving startups and enterprises.
Our clients have collectively raised over
$100M in VC
, and our enterprise partners include NASDAQ-listed companies.
We work fully remote, move fast with 1-week sprints, and focus on elegant solutions to complex problems.
#J-*****-Ljbffr

Postúlate en Kit Empleo: kitempleo.com.ar/empleo/pxztt

📌 General Ai Specialist - Pytorch, Mlflow, & Transformers (Buenos Aires)
🏢 OneSeven Tech (OST)
📍 Buenos Aires