Lead Machine Learning Engineer
Contractor · Remote in Latin America (only)· Full-time · initial 3 month project (high chance of extension)

We are seeking a Lead Machine Learning Engineer to support a large-scale Asset Image Analytics initiative for a major utilities client, focused on leveraging computer vision and AI to improve asset inspection, risk detection, and operational decision-making.

This role will contribute to the end-to-end development of ML pipelines, including image ingestion, labeling, model development, and deployment, enabling capabilities such as automated defect detection, asset classification, and predictive insights.

You will work closely with data scientists, engineers, and client stakeholders to build scalable solutions that transform imagery data into actionable intelligence for asset management.

Key Responsibilities

Design, develop, and deploy machine learning models, with a focus on computer vision (CV) use cases such as defect detection, classification, and change detection.
Build and maintain ML pipelines, including preprocessing, feature engineering, model training, and validation workflows.
Collaborate with cross-functional teams to support image labeling, dataset creation, and model training processes.
Develop and optimize image ingestion and metadata enrichment pipelines to support scalable analytics
Integrate ML outputs into downstream systems (e.g., asset management platforms, workflow tools) to enable automated insights and actions.
Improve model performance through continuous evaluation, tuning, and retraining strategies.
Support deployment and operationalization of models (MLOps), including monitoring and performance tracking.
Partner with stakeholders to translate business requirements into data science and ML solutions aligned to asset management use cases.
Contribute to the development of AI/ML best practices, governance, and scalable architecture.

Tech Stack

Programming: Python
Machine Learning & MLOps: Azure ML, MLflow, TensorFlow, Keras
Data & Analytics: Databricks
Infrastructure & Cloud: Microsoft Azure, Terraform, Kubernetes
CI/CD & Version Control: GitHub
Computer Vision: Image classification, defect detection, change detection
Data Engineering: Data pipelines, image ingestion, metadata enrichment

Qualifications

10-15+ years of experience as a machine learning (MLOps), data, and/or software engineer using Python in a production environment.
Experience building, deploying, and optimizing machine learning models.
Strong experience with Azure ML and Databricks/MLflow.
Strong experience with Terraform and Kubernetes.
Experience developing and deploying image models.
Experience building and managing robust CI/CD pipelines for machine learning workflows, including model training, evaluation, and deployment.
Knowledge of professional enterprise software development and practices, including software lifecycle, best coding practices, version control, architecture, testing, and deployment.
Familiarity with popular machine learning libraries and frameworks, including TensorFlow, Keras, etc.
Experience with GitHub.
Experience building data pipelines.
Strong collaboration and stakeholder engagement skills

Preferred qualifications

Master's degree/PhD in computer science or related field.
Consulting experience.

Why join us?

Competitive compensation paid in USD.
100% Remote.
Paid Time Off: 12 PTO days per year.
Local Holidays: All official holidays in your country.
Birthday Off.
Career Growth opportunities.
Recognition Program.
Paid approved leaves.

Lead Machine Learning Engineer (Azure &...