Machine Learning Engineer

Dearborn, Michigan Machine Learning Engineer #1065247

Employees in this job function are responsible for designing, building, deploying and scaling complex self-running ML solutions in areas like computer vision, perception, localization etc. They also automate and optimize the end-to-end ML model lifecycle using their expertise in experimental methodologies, statistics, and coding for tool building and analysis.

• Collaborate with business and technology stakeholders to understand current and future ML requirements
• Design and develop innovative ML models and software algorithms to solve complex business problems in both structured and unstructured environments
• Design, build, maintain and optimize scalable ML pipelines, architecture and infrastructure
• Use machine language and statistical modeling techniques such as decision trees, logistic regression, Bayesian analysis and others to develop and evaluate algorithms to improve product/system performance, quality, data management and accuracy
• Adapt machine learning to areas such as virtual reality, augmented reality, object detection, tracking, classification, terrain mapping, and others.
• Train and re-train ML models and systems as required
• Deploy ML models and algorithms into production and run simulations for algorithm development and test various scenarios
• Automate model deployment, training and re‑training, leveraging principles of agile methodology, CI/CD/CT (Continuous Integration/ Continuous Deployment/ Continuous Training) and MLOps
• Enable model management for model versioning and traceability to ensure modularity and symmetry across environments and models for ML systems

• GCP, Big Query, Python, Java, Cloud Infrastructure, Artificial Intelligence & Expert Systems

• Design, develop, test, and deploy the ISDP knowledge graph from the domain event store to Production using cloud-native data pipelines.
• Model and evolve the graph's entities and relationships as new data sources are onboarded. MCP serving layer
• Design, build, and operate the MCP server that exposes the ISDP graph layer and event store as tools to consumers — including GQL graph query, event-store query, and schema/DDL discovery.
• Define tool contracts, context, and guardrails so agents produce grounded, accurate, non-hallucinated responses over the graph.
• Ensure low-latency, secure, and cost-efficient serving for interactive and batch agent workloads. Reliability, monitoring & observability
• Own monitoring and observability of the graph layer and the MCP server — data freshness, pipeline health, query latency/cost, tool-call success rates, and answer quality.
• Instrument SLOs, dashboards, alerting, and tracing; drive incident response and continuous reliability improvements. Collaboration & data onboarding
• Partner with Data Engineers and Application Data Source Owners across Product Development, Manufacturing, Quality, and Supply Chain to ingest and validate their data into ISDP.
• Establish data contracts, schema validation, and quality checks; support source owners through onboarding, mapping to the ISDP logical model, and troubleshooting.
• Contribute to data governance, cataloging, and lineage for the graph and its sources.

• Engineer 2 Exp.:
  Practitioner: 1 coding language or framework. 7+ years in IT; 3+ years in development 2+ Years in AI and Graph Engineering
• Strong software engineering in Java, Python, with production-grade testing, CI/CD, and code quality practices.
• Hands‑on experience deploying data/AI systems to Production on a GCP-native stack:
  Vertex AI, Big Query, Dataflow / Apache Beam, Pub/Sub, Cloud Run / GKE, Cloud Storage, and Cloud Build / Artifact Registry.
• Experience with graph data modeling…