Machine Learning Engineer
- Posted 4 days ago
- Be among the first 10 applicants
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:
error analysis , Deduplication, Pytorch, Python, Cloud Storage, Statistical Analysis, data augmentation, model training, model-based labeling, distributed compute, Active Learning, VLMs, Hugging Face LLMs, ablation studies, dataset curation, benchmark evaluation, workflow orchestration, weak supervision, Batch Processing, inference evaluation, synthetic-data generation, Production Monitoring, data-quality measurement, diffusion models, multimodal models
Skills:
data engineering , Numpy, Pandas, Pytorch, Python, scikit-learn, ML pipelines
Skills:
Spark, Databricks, Python, LLM-based pipelines, LightGBM, Transformers, MLflow, semantic search
Skills:
S3, Tensorflow, Nlp, Pytorch, Docker, Python, Aws Services, Clustering, Redshift, Sql, Pig, Django, Git, Hive, Computer Vision, Anthropic Claude, Finetuning, AWS Bedrock, Azure OpenAI, Deep Learning architectures, GPTX.x, CNNs, Sagemaker, Classification, NLU, RAG pipelines, LangChain, LLMs, Regression, Llama, Transformers, Athena, Prompt Engineering
Skills:
classification), NLP (tokenization, Oops, C, C++, Os Fundamentals, Objective-c, Tensorflow, Pytorch, Swift, Python, language modeling, modern agentic and multi-agent systems, decoding, RAG architectures