Position: Machine Learning Engineer
Location: Dearborn, MI (Hybrid)
Employment Type: Full-Time Contract
Pay: $56-59 per hour
Description: We are seeking a Machine Learning Engineer to design, build, deploy, and scale advanced ML and AI solutions across areas such as computer vision, perception, localization, object detection, tracking, classification, terrain mapping, virtual reality, and augmented reality. This role will focus on machine learning, graph engineering, cloud-native architecture, MLOps, and AI-powered knowledge graph solutions within a Google Cloud Platform (GCP) environment.
Responsibilities:
- Partner with business and technology stakeholders to define current and future ML needs.
- Design, develop, train, retrain, and deploy ML models and algorithms for structured and unstructured data.
- Apply statistical and machine learning techniques such as decision trees, logistic regression, Bayesian analysis, and related methods to improve system performance, quality, accuracy, and data management.
- Build, maintain, and optimize scalable ML pipelines, infrastructure, and architectures.
- Run simulations and test scenarios to validate models and algorithms.
- Automate deployment, training, and retraining using MLOps and CI/CD/CT practices.
- Manage model versioning, traceability, and lifecycle governance across environments.
- Design, develop, test, and deploy the ISDP knowledge graph using cloud-native data pipelines.
- Model and evolve graph entities and relationships as new data sources are onboarded.
- Build and operate the MCP serving layer exposing graph and event-store capabilities through graph queries, event-store queries, and schema discovery tools.
- Define tool contracts, context, and guardrails to ensure grounded, accurate, and non-hallucinated AI responses.
- Deliver secure, scalable, low-latency, and cost-efficient serving for interactive and batch AI workloads.
- Own monitoring and observability for graph and MCP platforms, including data freshness, pipeline health, query performance, service reliability, tool-call success rates, and answer quality.
- Implement SLOs, dashboards, alerting, tracing, and incident response processes to drive continuous reliability improvements.
- Partner with Data Engineers and Data Source Owners across Product Development, Manufacturing, Quality, and Supply Chain organizations.
- Establish data contracts, schema validation, and data quality controls.
- Support onboarding, mapping, validation, and troubleshooting of enterprise data sources.
- Contribute to governance, cataloging, lineage, and enterprise data management initiatives.
Requirements:
- Bachelor’s Degree
- 7+ years of IT experience.
- 3+ years of software development experience.
- 2+ years of AI and Graph Engineering experience.
- Skills: Google Cloud Platform (GCP), BigQuery, Python, Java, Cloud Infrastructure, Artificial Intelligence & Expert Systems, Machine Learning, MLOps, CI/CD Pipelines, Graph Engineering
- Strong software engineering expertise in Java and Python with production-grade testing, code quality, and CI/CD practices.
- Hands-on deployment of data and AI solutions using GCP services including Vertex AI, BigQuery, Dataflow/Apache Beam, Pub/Sub, Cloud Run, GKE, Cloud Storage, Cloud Build, and Artifact Registry.
- Experience with graph data modeling and querying using BigQuery Property Graphs, Neo4j, Spanner Graph, or similar technologies.
- Experience with Vertex AI Agents, model serving, embeddings, and evaluation of agent response quality.
- Experience building LLM and agent solutions including tool usage, RAG/grounding, and model integrations through APIs.
- Familiarity with MCP or comparable agent tool protocols.
- Expertise with Cloud Monitoring, Logging, OpenTelemetry, dashboards, alerting, and observability best practices.
- Experience with Terraform, IAM, secrets management, and secure-by-default cloud engineering.
- Ability to work directly with data producers to model and validate enterprise and industrial data.
Preferred:
- Dataplex and Data Catalog for governance, lineage, and business glossary management.
- Streaming, CDC, event-driven, and event-sourced architectures.
- Development of user-facing applications and dashboards leveraging knowledge graph data.
- Experience supporting PLM, Product Development, Manufacturing Execution, Quality, or Supply Chain systems.
- Knowledge of data quality frameworks, schema evolution, and blue-green/zero-downtime deployment strategies.
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