Senior Machine Learning Engineer with Geospatial and Utilities Focus

Fundamentl; Argentina; Presencial; ARS 1.800.000 - 3.400.000; Jornada completa

Descripción de la vacante

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

You will own end-to-end ML pipelines—from image ingestion and labeling to model development and deployment—driving automated defect detection, asset classification, and predictive insights in asset management.

Formación

• 10-15+ years of experience in ML, data, and/or software engineering using Python in production.
• Experience building, deploying, and optimizing machine learning models.
• Strong experience with Azure ML and Databricks/MLflow.
• Experience developing and deploying image models.
• Experience building robust CI/CD pipelines for ML workflows.
• Familiarity with TensorFlow, Keras, and GitHub.

Responsabilidades

• Design, develop, and deploy ML models with a focus on computer vision use cases like defect detection and change detection.
• Build and maintain ML pipelines including preprocessing, feature engineering, model training, and validation.
• Collaborate with cross-functional teams to support image labeling, dataset creation, and model training processes.
• Develop and optimize image ingestion and metadata enrichment pipelines for scalable analytics.
• Integrate ML outputs into asset management platforms and workflow tools for automated insights.
• Improve model performance via evaluation, tuning, and retraining strategies.
• Support deployment and operationalization of models (MLOps) including monitoring and performance tracking.
• Translate business requirements into data science and ML solutions for asset management use cases.
• Contribute to AI/ML best practices, governance, and scalable architecture.

Conocimientos

Python

MLOps

Azure ML

Databricks/MLflow

Kubernetes

GitHub

CI/CD

TensorFlow

Keras

Educación

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

Herramientas

Terraform

Kubernetes

Descripción del empleo

We are looking for 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.

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.
• Strong collaboration and stakeholder engagement skills

Preferred qualifications

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