Senior ML Backend Engineer
New
J
JobgetherProperty Intelligence
MexicoFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonKubernetesCI/CDMLOps
Requirements
- Senior-level backend software engineering experience with a strong understanding of machine learning.
- Experience designing, building, and maintaining machine learning infrastructure, platforms, and tooling for large-scale training, evaluation, and deployment.
- Strong proficiency in Python.
- Proficiency with modern machine learning engineering tools, including deep learning frameworks, experiment tracking, version control, containerization, and automated workflows.
- Hands-on experience with MLOps practices such as CI/CD, model monitoring, reproducibility, data lineage, model governance, and production operations.
- Proven experience with cloud-native technologies, Kubernetes, distributed computing environments, and scalable infrastructure supporting machine learning workloads.
- Demonstrated understanding of artificial intelligence concepts and practical experience using AI tools, coding assistants, and LLM-based agents.
- Experience implementing AI-powered solutions to address business challenges, with awareness of responsible and ethical AI principles.
- Strong analytical, problem-solving, and communication skills.
- Ability to collaborate effectively with machine learning engineers, researchers, software developers, product teams, and business stakeholders.
- PhD in a STEM discipline is preferred; a Master's degree or Bachelor's degree with extensive relevant experience is also considered.
Responsibilities
- Build, maintain, and evolve scalable machine learning infrastructure supporting model development, training, evaluation, deployment, and monitoring.
- Design reliable, cost-effective ML pipelines, platforms, and engineering tooling for large-scale workloads.
- Partner with machine learning engineers and researchers to transition new models and approaches from prototypes into production-ready systems.
- Develop automation, testing, observability, monitoring, reproducibility, data lineage, and governance capabilities across ML environments.
- Evaluate and integrate new data sources, technologies, platforms, and tools that can improve model performance and operational efficiency.
- Collaborate with software engineering, technology, product, and commercial stakeholders to deliver scalable machine learning solutions.
- Apply AI-powered development tools, coding assistants, and LLM-based agents to automate workflows and improve engineering productivity.
- Ensure machine learning systems meet security, governance, responsible AI, and model risk management standards.
- Identify technical trade-offs and contribute to architectural decisions involving infrastructure scalability, reliability, performance, and cost.
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