Senior Machine Learning Engineer& DevOps Specialist

hace 5 días

Bogotá, Bogotá D.E., Colombia Kilimo Inc A tiempo completo

At Kilimo we are looking for an experienced Senior Machine Learning Engineer & DevOps Specialist with a solid background in deploying and scaling machine learning models and managing DevOps processes. The ideal candidate will have a proven track record in building and maintaining ML pipelines and expertise in DevOps practices to ensure seamless integration, deployment, and scalability of machine learning solutions. Extensive knowledge of modern machine learning frameworks and strong DevOps skills, particularly within climate and agricultural domains, are essential.

Key Responsibilities

Model Development and Deployment

  • Design, build, and optimize scalable machine learning models for climate and agricultural applications.
  • Implement production-ready ML pipelines, encompassing feature engineering, model training, and evaluation.
  • Deploy and monitor models in production environments to ensure performance, reliability, and scalability.
  • Create and maintain databases through data lakes for efficient data storage and access.
  • Utilize and generate ETLs to extract, transform, and load data, ensuring compatibility and accessibility throughout the ML pipeline.

MLOps and DevOps Integration

  • Develop and maintain automated systems for model retraining, versioning, and monitoring using MLOps best practices.
  • Design and implement CI/CD pipelines tailored for machine learning workflows to streamline development and deployment processes.
  • Manage infrastructure as code (IaC) using tools like Terraform or Ansible to provision and maintain cloud resources.
  • Ensure robust monitoring, logging, and alerting systems are in place for both ML models and underlying infrastructure.

Infrastructure Management

  • Oversee the deployment and orchestration of containerized applications using Docker and Kubernetes.
  • Optimize cloud infrastructure (AWS, Azure, GCP) for cost, performance, and scalability to support ML workloads.
  • Collaborate with DevOps teams to enhance system reliability, security, and efficiency.

Domain-Specific Applications

  • Apply machine learning techniques to analyze climate, weather, and agricultural data.
  • Develop predictive models for crop growth, irrigation optimization, and environmental sustainability.
  • Utilize remote sensing data and satellite imagery for geospatial analysis and decision-making.

Research and Optimization

  • Stay updated on the latest trends in machine learning and DevOps, applying state-of-the-art techniques to projects.
  • Optimize models and infrastructure for performance, scalability, and accuracy in large-scale deployments.
  • Conduct experiments to test and validate hypotheses for ML-driven and DevOps-enhanced solutions.

Minimum Qualifications

  • 5+ years of experience in Machine Learning Engineering, DevOps, or a related field.
  • Strong programming skills in Python and familiarity with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
  • Hands-on experience with ML pipeline tools (e.g., MLflow, Kubeflow, Airflow) and DevOps tools (e.g., Jenkins, GitLab CI, Docker, Kubernetes).
  • Proficiency in cloud platforms (AWS, Azure, GCP) and containerization technologies.
  • Solid understanding of data preprocessing, feature engineering, model evaluation techniques, and DevOps best practices.
  • Experience with infrastructure as code (IaC) tools such as Terraform or Ansible.
  • Strong understanding of CI/CD workflows and their integration into ML pipelines.

Domain Expertise

  • Knowledge of climate science, agronomy, or related fields.
  • Familiarity with datasets such as satellite imagery, weather patterns, or agricultural productivity metrics.

Educational Background

  • Bachelor's or Master's degree in Computer Science, Machine Learning, DevOps, or a related field.
  • Advanced certifications in Machine Learning, DevOps, MLOps, or related areas are a plus.

Problem-Solving Skills

  • Proven ability to design and implement efficient solutions for complex problems at the intersection of ML and DevOps.
  • Experience in optimizing machine learning workflows and infrastructure for performance and scalability.

Preferred Skills

  • Familiarity with geospatial analysis and remote sensing technologies.
  • Experience with big data frameworks (e.g., Apache Spark).
  • Understanding of ethical AI principles and practices.
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