Senior AI/ML Engineer — Data Pipelines & Scale

BlackLine; New York (NY); Hybrid; USD 145,000 - 182,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

BlackLine is seeking a Senior AI/ML Engineer to design, build, and optimize data pipelines powering AI-driven accounting agents. You will lead PySpark ETL development, connect client data via APIs and internal connectors, and ensure data accuracy at scale across cloud environments.

You’ll guide ML deployment, collaborate with data science and security teams, and help define governance and observability practices for reliable AI systems.

Qualifications

• 3+ years of programming experience in Python, Java or Scala.
• Expertise with ML frameworks (TensorFlow, PyTorch, scikit-learn) and orchestration tools (Airflow, Kubeflow, Vertex AI, MLflow).
• Production pipelines experience across cloud ecosystems (GCP, AWS, Azure).
• Familiarity with LangChain, LangGraph, ADK or similar agentic runtime management.

Responsibilities

• Lead data pipeline development with PySpark ETL pipelines and data quality.
• Establish robust data connections to client sources via APIs and tools like FiveTran and Plaid.
• Monitor, test, and automate data accuracy and pipeline reliability.
• Design scalable AI/ML deployment and model integration across platforms.

Skills

Python

Java

Scala

TensorFlow

PyTorch

scikit-learn

CI/CD

IaC

DevSecOps

Observability

LangChain

LangGraph

ADK

Docker

Kubernetes

Prometheus

Grafana

New Relic

Tools

Airflow

Kubeflow

Vertex AI

MLflow

FiveTran

Plaid

APIs

Docker

Kubernetes

LangChain tooling

LangGraph tooling

Prometheus

Grafana

New Relic

Job description

BlackLine is seeking a Senior AI/ML Engineer to design, build, and optimize data pipelines powering AI-driven accounting agents. You will lead PySpark ETL development, connect client data via APIs and internal connectors, and ensure data accuracy at scale across cloud environments.

You’ll guide ML deployment, collaborate with data science and security teams, and help define governance and observability practices for reliable AI systems.