Senior AI/ML Data Engineer

Saved job description

What You’ll Do

Data Engineering & Pipeline Development (Primary) — 60%

Architect, build, and maintain batch and streaming data pipelines using BigQuery, dbt, Airflow/Cloud Composer, and Pub/Sub

Design and implement layered data models, semantic layers, and modular pipelines that scale as business needs evolve

Establish and enforce best practices for data quality, observability, lineage, and schema governance

Optimize BigQuery for performance and cost efficiency, including partitioning, clustering, and workload-aware modeling

Work with both structured data and unstructured data such as web logs, call center transcripts, images, and video when required by the use case

Leverage BQML and related data science capabilities for use cases such as anomaly detection, classification, and operational decision support

Deliver reliable, scalable, and high-performing pipelines that enable downstream ML, analytics, and operational applications

ML/AI Platform Engineering — 20%

Convert prototype notebooks and models into production-grade, versioned, testable Python packages

Deploy and manage training and inference workflows on GCP using Cloud Run, GKE, and Vertex AI

Implement CI/CD, model versioning, rollback strategies, and operational guardrails for ML systems

Evaluate emerging GCP and third-party products; build shared libraries, templates, and internal tooling that accelerate delivery across teams

Enable ML teams to ship faster with fewer operational failure points

Applied AI & Real-Time Decisioning — 20%

Support real-time, event-driven inference and streaming feature delivery for mission-critical decisions, including recommendation systems, dynamic experimentation, and agentic AI use cases

Contribute to internal LLM-based assistants, retrieval-augmented generation (RAG) systems, and automation agents

Implement model monitoring, drift detection, alerting, and performance tracking frameworks

Evaluate and apply graph-based data patterns where they improve recommendations, relationship analysis, knowledge retrieval, or decision intelligence

Cross-Functional Collaboration

Partner with data scientists, analysts, and engineers to operationalize models, semantic layers, and data products into maintainable production systems

Collaborate with pricing, digital product, analytics, and business teams to stage rollouts, support experiments, and define success metrics

Participate in architecture reviews, mentor engineers, and communicate technical trade-offs clearly

Contribute to an engineering culture grounded in ownership, curiosity, thoughtful debate, and continuous learning

What We’re Looking For

We are looking for someone who is not only technically strong, but also:

Passionate about building high-quality systems

Hardworking and dependable, with strong ownership of outcomes

Eager to learn, experiment, and deepen expertise over time

Comfortable in a fast-paced environment where priorities can shift quickly

Open to collaboration, feedback, and healthy technical debate

Interested in growing both technical depth and leadership capability over the long term

Qualifications

MS in Computer Science with 4+ years of experience, or BS in Computer Science with 6+ years of experience

3+ years of hands-on experience building data pipelines in a code-first environment using Python, SQL, and dbt

1+ year of experience with real-time or event-driven systems such as Pub/Sub, Dataflow, or comparable streaming frameworks

2+ years of experience owning technical decisions or helping lead engineering direction

1+ year of hands-on experience with graph databases, including graph data modeling, relationship-centric querying, or graph-based problem solving

Preferred Experience

Experience with GCP and/or AWS

Experience with ML platform tooling or monitoring such as MLflow, Evidently, or Vertex AI

Knowledge of semantic search, vector embeddings, LLM orchestration, or RAG workflows

Experience with graph database technologies such as Neo4j, Neptune, or similar platforms

Familiarity with use cases involving knowledge graphs, entity relationships, recommendation systems, fraud patterns, or connected data analysis

Domain experience in pricing, recommendations, forecasting, or large-scale customer analytics

Familiarity with geospatial data or map-based modeling

Some JavaScript experience for lightweight UI or prototyping work

Why This Role Will Excite You

You’ll work in a fast-moving environment where decisions are made quickly and your work drives real business outcomes

You’ll own meaningful parts of the platform, not just execute isolated tasks

You’ll build end-to-end solutions—from ingestion and transformation to ML inference and production integration

You’ll have the opportunity to learn quickly, expand your scope, and grow your technical leadership

You’ll join a team that values curiosity, execution, and continuous improvement