Big Rattle Technologies - Machine Learning Developer

Description :

Location : Remote (Company in Mumbai).

Company : Big Rattle Technologies Private Limited.

- Immediate Joiners only.

Summary :

The ML Developer will design, build, and maintain machine learning models and data pipelines powering core business use cases.

The role is hands-on with Python for model development, feature engineering, and pipeline automation, leveraging Azure ML, and Azure DevOps.

Success means robust, production-grade models with proven business impact, traceable lineage, and operational excellence at scale.

Key Responsibilities :

- Translate model prototypes from Data Scientists into Azure ML production pipelines, including data ingestion, training, inference, and retraining.

- Build and iterate on ML models (forecasting/classification/regression) using modern ML frameworks (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow).

- Develop robust feature pipelines (deterministic code, modular definitions, reusability) using Pandas/PySpark and orchestrate in AML Pipelines Jobs.

- Design experiments with proper sampling, train-test splits, cross-validation, and metrics selection (e.g., RMSE, AUC, MAPE).

- Implement model selection, champion/challenger promotion, and versioning strategies.

- Document experiment results for reproducibility and regulatory compliance.

Model Operationalization & Monitoring :

- Productionize models as batch or real-time endpoints via Azure ML.

- Implement model validation gates (drift/shift, prediction distribution checks, champion vs. challenger results).

- Set up model monitoring dashboards for latency, prediction freshness, data drift, and feature importance tracking.

- Integrate model deployment/test harnesses with Azure DevOps pipelines for CI/CD.

- Develop FastAPIs to invoke and consume ML models.

Data Engineering & Quality :

- Profile, clean, and transform raw data from Snowflake, SQL, and third-party sources.

- Implement checks for data quality (nulls, schema validation, outlier handling, time alignment,

duplicate detection).

- Automate feature extraction and maintain feature store consistency.

Collaboration & Quality Ops :

- Work with Product, Data, and QA teams to agree on model acceptance criteria and experiment reviews.

- Contribute to defect taxonomy (data/model/serving), pipeline observability, and SLO dashboards.

- Publish model performance reports and SLI/SLO summaries for stakeholders.

Required Skills (hands-on experience in the following) :

- Advanced proficiency in Python (pandas, NumPy, ML frameworks), SQL, and cloud data tools.

- Understanding of model validation, drift detection, and online monitoring.

- Experience with feature stores, CI/CD (Azure DevOps), and API development (FastAPI/Flask).

Required Qualifications :

- 5+ years developing data-focused solutions (3+ years in ML modeling and operations).

- Certification in Azure Data or ML Engineer Associate is a plus.