ML Engineer
Bengaluru, Karnataka, IndiaJobNo compensation foundPosted 1mo agoVerified open 3 days ago
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At a glance
Compensation
No compensation found
Location
Bengaluru, Karnataka, India
Work Authorization
Not specified
Job overview
The role seeks an ML Engineer who will design, build, and ship production‑ready machine‑learning models, fine‑tune large language models, and develop robust feature‑engineering pipelines for structured data, while ensuring model quality through evaluation, bias mitigation, and explainability. The candidate will also integrate MLOps tooling, manage model serving frameworks, and collaborate on A/B testing and shadow deployments to drive real‑world outcomes such as risk and fraud prediction.
Skills & qualifications
RequiredNice to have
Skills
Artificial IntelligenceData EngineeringAWS SageMakerFeature ExtractionTensorFlowCI/CDMachine LearningMLFLowPyTorchTritonTorchServeTensorFlow ServingKubeflowSageMakerFeature EngineeringData PipelinesModel EvaluationA/B TestingBias Fairness ExplainabilityLoRAPEFT
Qualifications
3+ Years Experience
Full job description
Requirements:
- 3-5 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).
ML-Specific Expertise:
- 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.
You've read the whole posting — now see how you match it.