Senior ML Engineer (AI Engineering)

TL;DR

Senior ML Engineer (AI Engineering): Designing, developing, and deploying production-grade machine learning solutions for clients with an accent on complex ML problems, scalable ML pipelines, and model optimization. Focus on building LLM-based applications, RAG systems, and cloud-native ML architectures on AWS and GCP.

Location: Must be based in Colombia, including Medellín, Bogotá, Cali, Barranquilla, and Bucaramanga.

Company

hirify.global is seeking a Senior ML Engineer to design, develop, and deploy production-grade machine learning solutions for clients.

What you will do

• Design and implement end-to-end ML solutions from experimentation to production.
• Build scalable ML pipelines and infrastructure, optimizing model performance and reliability.
• Write clean, maintainable, production-quality code and conduct rigorous experimentation.
• Mentor junior to mid-level ML engineers and conduct code reviews.
• Collaborate with cross-functional teams and contribute to internal ML practice development.
• Participate in technical discussions and architectural decisions, proposing improvements to existing solutions.

Requirements

• Strong understanding of ML fundamentals, including supervised, unsupervised, and reinforcement learning.
• Expertise in model development, feature engineering, training, evaluation, and hyperparameter tuning.
• Proficiency with ML frameworks like TensorFlow, PyTorch, and experience with Deep Learning (CNNs, RNNs, Transformers).
• Experience building production LLM-based applications, prompt engineering, RAG systems, and vector databases.
• Advanced proficiency in Python for ML applications, data manipulation with pandas/numpy, and SQL.
• Experience building ETL/ELT pipelines, distributed computing with Spark, and MLOps practices like model deployment, containerization (Docker), CI/CD, and monitoring.
• Strong experience with AWS ML services (SageMaker, Lambda) and GCP ML/data services, understanding cloud-native ML architectures and Infrastructure as Code (Terraform, CloudFormation).

Nice to have

• Practical experience with AWS stack (e.g., Amazon SageMaker, ECR, EMR, S3, AWS Lambda).
• Practical experience with deep learning models.
• Experience with taxonomies or ontologies.
• Practical experience with machine learning pipelines to orchestrate complicated workflows.
• Practical experience with Spark/Dask, Great Expectations.