ML Engineer

Job#: 3029329 Job Description: ML Engineer Location: 100% Remote (client sits in Dearborn, MI) Employment Type: Contract Position Description Employees in this role are responsible for designing, building, and maintaining data solutions, including data infrastructure and data pipelines, to collect, store, process, and analyze large volumes of data efficiently and accurately. Key Responsibilities Collaborate with business and technology stakeholders to understand current and future data requirements. Design, build, and maintain reliable, efficient, and scalable data infrastructure for data collection, storage, transformation, and analysis. Plan, design, build, and maintain scalable data solutions, including data pipelines, data models, and applications, to ensure efficient and reliable data workflows. Design, implement, and maintain existing and future data platforms such as data warehouses, data lakes, and data lakehouse architectures for both structured and unstructured data. Design and develop analytical tools, algorithms, and programs to support data engineering activities, including scripting and task automation. Ensure optimal system performance and proactively identify opportunities for improvement. Skills Required GCP, Big Data, Data Warehousing, Artificial Intelligence & Expert Systems, API Google Cloud Platform (GCP) Experience deploying and managing services on GCP, including Compute Engine, Cloud Storage, IAM, and Cloud Functions. Example: Designing and implementing a cloud‑native application architecture using Google Kubernetes Engine (GKE) with Cloud SQL and Pub/Sub. Big Data Experience working with large‑scale data processing frameworks such as Apache Spark, Dataflow, or BigQuery. Example: Building ETL pipelines that process terabytes of daily event data and transform it for downstream analytics. Data Warehousing Experience designing and maintaining data warehouse solutions (e.g., BigQuery, Snowflake, Redshift). Example: Modeling a star schema for a retail analytics platform supporting reporting on sales, inventory, and customer behavior. Artificial Intelligence & Expert Systems Experience developing or integrating AI/ML models and rule‑based expert systems. Example: Building a classification model using Vertex AI to predict customer churn or implementing a rule engine to automate underwriting decisions. API Experience designing, building, and consuming RESTful or gRPC APIs. Example: Developing a versioned REST API with OAuth 2.0 authentication to serve as an integration layer between a mobile application and backend microservices. Skills Preferred Google Cloud Platform Familiarity with advanced GCP services beyond core compute and storage, including Vertex AI, Dataflow, Cloud Composer (Airflow), and BigQuery ML. Example: Using Cloud Composer to orchestrate scheduled data pipelines that feed into a BigQuery data warehouse. Experience Required Senior Engineer 10+ years of Data Engineering work experience Experience Preferred As a Senior Data Engineer, you will architect and scale end‑to‑end data pipelines on GCP, transforming complex telemetry and enterprise data into high‑quality, analytics‑ready assets using Medallion architectures. You will lead the implementation of robust CI/CD workflows, rigorous data governance, and security controls, while mentoring junior talent and driving engineering best practices. By collaborating with cross‑functional stakeholders and optimizing cloud performance, you will ensure the data platform remains secure, cost‑effective, and highly available to power critical business insights. Operational Excellence: Using Terraform, Git, and Airflow to ensure reproducible, secure, and cost‑optimized cloud infrastructure. Governance & Quality: Prioritizing data lineage, PII protection, and observability to maintain high trust in data assets. Collaboration: Acting as a bridge between technical teams (Data Science, Security) and business stakeholders to deliver self‑service analytics. Strong understanding of Generative AI principles and architectures, including Large Language Models (LLMs) and Retrieval‑Augmented Generation (RAG) systems. Proven experience building and deploying RAG systems, including the use of vector databases. Proficiency in Python programming. Strong experience using SQL for data mani