Senior Machine Learning Engineer - Kubogent

About Us Aivar Innovations is an AI-native services company and AWS Preferred Partner building governed, production-grade agentic AI systems. We partner with enterprises to deploy intelligent agents that automate complex business processes—from intelligent customer interactions to enterprise knowledge systems. Experience: 4-8 years | Machine Learning, LLM Training, Inference & MLOps The Role: Kubogent is an AI/ML platform for teams that need to train, fine-tune, deploy, and operate machine learning models across Kubernetes environments. We are looking for a Senior Machine Learning Engineer who can help turn real-world ML workflows into reliable product capabilities inside Kubogent. This is a hands-on engineering role. You will work across the model lifecycle - from preparing data and training models to building reusable training jobs, inference workflows, experiment tracking, evaluation, and model lifecycle pipelines. You should be comfortable working directly with models and data, while also thinking about how those workflows should be exposed as repeatable, production-grade platform capabilities for other teams to use. You will work closely with the engineers building Kubogent's Kubernetes, accelerator, scheduling, storage, observability, and platform layers. Your job is not only to train models, but to help define how training and inference should work as a product. What you'll do ● Build Kubogent's model training capabilities. Design and implement reusable training and fine-tuning workflows for different classes of machine learning models, including large language models. ● Build training-job abstractions. Help define how users configure datasets, models, hyperparameters, compute requirements, checkpoints, outputs, and distributed training behavior, and translate those workflows into robust Kubogent training jobs. ● Build inference workflows. Work on model loading, serving, batching, runtime configuration, evaluation, scaling behavior, and performance characteristics for different model types and inference runtimes. ● Train and fine-tune models. Perform hands-on model development, training, fine-tuning, evaluation, debugging, and iteration using modern machine learning frameworks and tooling. ● Own data preparation workflows. Work with raw datasets, understand data quality problems, clean and transform data, build preprocessing pipelines, create training/evaluation splits, and ensure datasets are reproducible and usable by downstream training jobs. ● Build MLOps pipelines. Implement repeatable workflows covering experimentation, training, evaluation, model registration, versioning, promotion, deployment, and rollback. ● Integrate experiment and model lifecycle tooling. Build integrations with tools such as MLflow for experiment tracking, metrics, artifacts, model metadata, model registries, and reproducibility. ● Evaluate models systematically. Define and implement evaluation pipelines, quality metrics,…