Machine Learning Engineer
Machine Learning Engineer
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Job Description
Responsibilities
- Build and train proprietary models on Oolka's data for repayment-likelihood scoring, negotiation outcome prediction, and credit-risk signals.
- Own the full model lifecycle : data collection, feature engineering, training, validation, deployment, and monitoring.
- Fine-tune LLMs and smaller models for domain-specific tasks, structured extraction from credit reports, and negotiation dialogue quality.
- Build and maintain the evaluation framework that catches model quality regressions before they ship.
- Build feature pipelines from credit bureau, transaction, and repayment data.
- Design and operate model serving : batching, quantisation, versioning, and rollback for models you own.
- Monitor for model drift, degradation, and bias in production, and own the retraining loop.
- Partner with the AI engineering team; they own how models get built and improved; they own how models get served in the live product.
- 3+ years building and shipping ML models in production, not just integrating third-party AI APIs.
- Hands-on experience training and fine-tuning models (PyTorch or TensorFlow), classical ML and/or LLM fine-tuning.
- Strong feature engineering and data pipeline experience on structured/tabular data.
- Experience with model-serving frameworks (Triton, TorchServe, TensorFlow Serving) and inference optimisation : batching, quantisation, and distillation.
- Familiarity with MLOps tooling, experiment tracking, model registries, and CI/CD for models (MLflow, Kubeflow, SageMaker, or equivalent).
- Built and shipped models predicting real-world outcomes (risk, churn, ranking, or similar); credit, lending, or fraud experience is a strong plus.
- Experience with offline and online model evaluation, held-out test sets, A/B testing, and shadow deployment.
- Understanding of LLM fine-tuning approaches (LoRA/PEFT) and when fine-tuning beats prompting.
- Comfortable with the bias, fairness, and explainability bar that comes with models touching credit decisions.
- Has debugged a model quality regression in production and traced it back to a data or training root cause.
More Info
Key Skills
experiment tracking
quantisation
TensorFlow Serving
MLflow
model-serving frameworks
batching
inference optimisation
classical ML
SageMaker
LLM fine-tuning
MLOps tooling
Kubeflow
model registries
TorchServe
feature engineering
About Company
Skills:
Tensorflow, Pytorch, Python, MLops, Spark, Databricks, GPU infrastructure, model versioning, event tokenization, efficient attention, attention-based architectures, positional embeddings, mixed-precision training, self-supervised pretraining, gradient accumulation, automated retraining, experiment tracking, masked modeling, deep learning models, distributed strategies, contrastive learning, check-pointing, downstream fine-tuning, Production Monitoring
Skills:
Tensorflow, Nlp, Jax, Pytorch, Python, ASR, TTS, Llm
Skills:
Machine Learning, Deep Learning, Tensorflow, Pytorch, Opencv, Python, Computer Vision, GenAI, Prompting, LLMs, GenAI systems, OCR engines, PIL, Fine-tuning
Skills:
data engineering , Numpy, Pandas, Pytorch, Python, Testing and release, scikit-learn, Development processes, ELT data pipelines, ML research methodologies, Build processes, Production ML systems, Source control management, ML pipelines
Skills:
Java, Docker, Kubernetes, Python, TFX, AWS SageMaker, Kubeflow, Google Vertex AI, Go, MLflow, Metaflow