Senior AI/ML Engineer
B
BesedoContent moderation
This role is 100% remote, so you can work from anywhere in the EUContractSenior
Salary not disclosed
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Job Details
- Experience
- 6+ years (or equivalent) experience across ML engineering and MLOps/production ML
- Required Skills
- DockerPythonPyTorchCI/CDscikit-learnMLOps
Requirements
- Master’s, PhD, or equivalent experience in Computer Science, Engineering, Data Science, or a related field.
- Strong foundations in mathematics, statistics, and machine learning.
- 6+ years or equivalent experience across ML engineering and MLOps/production ML.
- A proven track record delivering high-impact systems in production.
- Expertise deploying models to production, including scalable serving patterns, model packaging, inference optimization, observability, and safe rollout strategies.
- Experience optimizing model performance and efficiency, including latency and throughput tuning, batching, quantization, ONNX/TensorRT, GPU/CPU optimization, memory, and cost tuning.
- Strong proficiency in Python.
- Hands-on experience with PyTorch and scikit-learn, and optimization/runtime stacks such as ONNX and TensorRT.
- Strong grasp of CI/CD for ML, containerization with Docker, and reproducible workflows.
- Experience with monitoring and reliability, including metrics, logging, tracing, model/data drift, alerting, SLOs, and incident response practices.
Responsibilities
- Handle the production lifecycle of ML systems, including training, packaging, deployment, monitoring, and controlled rollouts.
- Design, build, and improve training and retraining pipelines for data ingestion, labeling and QA loops, feature generation, experiment tracking, and reproducibility.
- Build and improve high-performance inference services and deployment patterns.
- Optimize inference using ONNX/TensorRT, quantization, batching, and GPU/CPU tuning.
- Partner with DevOps/SRE on scaling, reliability, incident readiness, observability, and alerting.
- Own model versioning, CI/CD, and release workflows for reproducible and safe production delivery.
- Strengthen data and model monitoring for performance, drift, and quality, and implement feedback loops.
- Communicate results and trade-offs to stakeholders.
- Evaluate model quality and implement production-grade validation aligned with evolving data and business needs.
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