
Full-stack Data + ML Engineer
3 days ago
We are looking for a Full-stack Data + ML Engineer (Mid-Level) to join our Data & ML Team. The ideal candidate will bring strong experience in building data pipelines, managing data infrastructure, and deploying ML models into production. This is a hands-on, high-impact role that blends data engineering and machine learning to drive actionable intelligence across the business.
Role & responsibilities:
Data Engineering (50%)
- Design and build robust ELT/ETL pipelines from app, events, and 3rd-party data sources (batch & streaming).
- Create well-modeled data layers (staging/marts) with testing, documentation, and version control (e.g., dbt).
- Operate and optimize data warehouses/lakes, ensuring data lineage, quality checks, and secure access (PII compliance).
- Contribute to observability, cost tracking, and on-call support for data pipelines.
ML/AI (50%)
- Frame business problems, prepare datasets, and train/evaluate ML models for production use.
- Build and maintain inference services/APIs (e.g., FastAPI, Triton, KServe) with defined latency and cost targets.
- Implement LLM pipelines (RAG), manage retrieval evaluation, prompt optimization, and safety guardrails.
- Work on classic ML use cases such as risk scoring, recommendation, churn, uplift modeling, and A/B testing.
- Monitor model drift, data integrity, and performance; maintain detailed runbooks and documentation.
Preferred candidate profile
- 4 - 6 years delivering production data systems and ML features.
- Strong SQL + Python; hands-on with dbt and an orchestrator (Airflow/Prefect/Dagster).
- Experience with a cloud DW (Snowflake/BigQuery/Redshift) and lake formats (Parquet/Delta/Iceberg).
- ML toolchain: PyTorch/TF, scikit-learn, experiment tracking (MLflow/W&B).
- Model serving know-how; comfort with Docker, CI/CD, and basic Kubernetes concepts.
- Clear communication, documentation habits, and pragmatic trade-offs.
Nice to have
- Streaming (Kafka/Flink/Spark Structured Streaming) or CDC (Debezium).
- Feature stores (Feast) and vector DBs (pgvector/FAISS/Weaviate) for LLM/RAG.
- Fintech exposure: lending/underwriting, bureau/alt-data ingestion, model risk controls, data retention & compliance.
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