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Senior Machine Learning Engineer - Kubogent

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Senior Machine Learning Engineer - Kubogent in Others

  • Aivar Innovations Private Limited
  • Full time
  • Email
  • Bengaluru, India

Responsibilities

  • Build reusable training and fine-tuning workflows and training-job abstractions for machine learning models and large language models.
  • Develop inference workflows covering model loading, serving, batching, runtime configuration, evaluation, scaling, latency, and throughput.
  • Perform data preparation, model training, fine-tuning, evaluation, debugging, and iteration.
  • Implement reproducible MLOps pipelines for experimentation, evaluation, model registration, versioning, promotion, deployment, and rollback.
  • Integrate experiment tracking and model lifecycle tooling, including metrics, artifacts, metadata, registries, and reproducibility.
  • Define evaluation pipelines, quality metrics, benchmark datasets, regression checks, and model acceptance criteria.
  • Optimize memory use, accelerator utilization, throughput, latency, batch sizing, precision, checkpointing, and parallelism.
  • Make ML workflows production-grade through failure recovery, observability, artifact management, configuration, and reproducibility.
  • Help shape Kubogent APIs, workflows, defaults, templates, and reusable platform primitives.

Requirements

  • 4-8 years of relevant engineering experience with substantial hands-on work building and training machine learning systems.
  • Hands-on experience training or fine-tuning large language models, including tokenization, dataset preparation, checkpoints, evaluation, and practical LLM constraints.
  • Strong data handling skills covering cleaning, normalization, filtering, transformation, sampling, labeling, validation, and train/validation/test preparation.
  • Strong Python skills and hands-on experience with mainstream ML frameworks such as PyTorch or TensorFlow.
  • Experience with the modern LLM ecosystem, including Hugging Face Transformers and related model, tokenizer, dataset, and training tooling.
  • Working experience with MLflow or comparable MLOps tooling, including experiment tracking, artifacts, parameters, metrics, model versioning, registries, and reproducible runs.
  • Experience with model serving or inference runtimes and understanding of batching, latency, throughput, memory use, model loading, precision, and accelerator constraints.
  • Ability to turn experimental ML code into maintainable libraries, services, jobs, pipelines, or platform components.
  • Understanding of reproducibility across dataset, code, environment, configuration, seeds, checkpoints, artifacts, and metadata.
  • Ability to debug across models, data, frameworks, runtimes, hardware, and surrounding systems.
  • Preferred experience with distributed training technologies such as PyTorch Distributed, FSDP, DeepSpeed, or Ray Train.
  • Preferred experience with parameter-efficient fine-tuning such as LoRA; LLM serving runtimes such as vLLM, NVIDIA Triton, TensorRT-LLM, or TGI; GPUs and ML accelerators; ML workloads on Kubernetes; ML workflow systems; large-dataset processing tools; model evaluation, benchmark, safety, or regression pipelines; or ML and developer platforms.

Aivar Innovations Private Limited

Aivar Innovations Private Limited pioneers cutting‑edge solutions that transform ideas into tangible products, blending creativity with precision engineering. Our collaborative culture empowers teams to tackle complex challenges, delivering reliable, user‑centric technologies that drive sustainab...

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