Full-stack Data + ML Engineer (Mid-Level)

About the company

Our client is a global, AI-powered EdFinTech company, dedicated to simplifying and democratizing access to education financing for students pursuing studies abroad. By leveraging technology and strategic partnerships, our client aims to provide seamless, transparent, and affordable financial solutions to empower the next generation of global leaders.

Responsibilities

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.

Responsibilities:

Data Engineering (?50%)

Design and build robust ELT/ETL pipelines from app, events, and 3rd-party data sources (batch & streaming).

Create well-modelled data layers (staging/marts) with testing, documentation, and version control (e.g., dbt).

Operate and optimize data warehouses/lakes, ensuring data lineage, qualitychecks, 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.

Qualifications

4 - 6 years of experience delivering production-grade data systems and MLfeatures.

Strong expertise in SQL and Python.

Hands-on experience with dbt and an orchestration tool (Airflow/Prefect/Dagster).

Proficiency with cloud data warehouses (Snowflake/BigQuery/Redshift) and data lake formats (Parquet/Delta/Iceberg).

ML toolchain proficiency: PyTorch/TensorFlow, scikit-learn, MLflow/W&B.

Familiarity with model serving, Docker, CI/CD, and Kubernetes concepts.

Strong communication skills, documentation habits, and ability to make pragmatic trade-offs.

Nice to Have:

Experience with streaming frameworks (Kafka/Flink/Spark Structured Streaming) or CDC tools (Debezium).

Familiarity with feature stores (Feast) and vector databases (pgvector/FAISS/Weaviate) for LLM/RAG use cases.

Exposure to FinTech/lending domainsunderwriting, bureau & alt-data ingestion, model risk controls, and data compliance.

Salary

Open for the right candidate