Senior ML Engineer

• Handle the production lifecycle of ML systems, including training, packaging, deployment, monitoring, and controlled rollouts
• Design, build, and continuously improve training and retraining pipelines covering data ingestion, labeling/QA loops, feature generation, experiment tracking, and reproducibility
• Build and improve high-performance inference services and deployment patterns
• Drive inference optimization using ONNX/TensorRT, quantization, batching, and GPU/CPU tuning, with measurable latency and cost improvements
• Partner with DevOps/SRE to ensure scaling, reliability, incident readiness, observability, and alerting
• Own model versioning, CI/CD, and release workflows to ensure reproducibility and safe production delivery
• Strengthen data and model monitoring for performance, drift, and quality, and implement feedback loops for continuous improvement
• Communicate results and trade-offs to stakeholders, translating technical outcomes into business value
• Ensure model quality through rigorous evaluation and production-grade validation aligned with evolving data and business needs

• Master’s, PhD, or equivalent experience in Computer Science, Engineering, Data Science, or a related field, with strong foundations in mathematics, statistics, and ML
• 6+ years of experience across ML engineering and MLOps/production ML, or equivalent experience
• 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/throughput tuning, batching, quantization, ONNX/TensorRT, GPU/CPU optimization, memory, and cost tuning
• Strong proficiency in Python
• Hands-on experience with PyTorch, scikit-learn, ONNX, and TensorRT
• Strong understanding of CI/CD for ML, GitLab CI, Docker, and reproducible workflows
• Experience with monitoring and reliability, including metrics, logging, tracing, model/data drift, alerting, SLOs, and incident response practices
• Experience with Kubernetes, AWS, FastAPI or similar Python serving frameworks, SageMaker, service meshes, feature stores, and model registries