Machine Learning Engineer – MLOps & Cloud Data Engineering

Location: Allen Park (MI) |
Employment Type: Full Time - 40 hours per week |
Job Level: T3 |
Work Preference: Hybrid |
Job Code: 245427

We are seeking a Machine Learning Engineer II responsible for designing, building, deploying, and scaling complex machine learning solutions in areas such as computer vision, perception, and localization. This role will also focus on automating and optimizing the end-to-end machine learning model lifecycle using experimental methodologies, statistics, software development, and MLOps practices.

The position will work closely with business and technology stakeholders to develop machine learning models, scalable pipelines, cloud infrastructure, and production-ready AI solutions.

Collaborate with business and technology stakeholders to understand current and future machine learning requirements.

Design and develop innovative machine learning models and software algorithms to solve complex business problems in structured and unstructured environments.

Design, build, maintain, and optimize scalable machine learning pipelines, architecture, and infrastructure.

Apply machine learning and statistical modeling techniques, including decision trees, logistic regression, Bayesian analysis, and other methods, to develop and evaluate algorithms.

Apply machine learning to areas such as virtual reality, augmented reality, object detection, tracking, classification, and terrain mapping.

Train and retrain machine learning models and systems as required.

Deploy machine learning models and algorithms into production and run simulations for algorithm development and testing.

Automate model deployment, training, and retraining using Agile methodology, CI/CD/CT (Continuous Integration, Continuous Deployment, and Continuous Training), and MLOps principles.

Enable model management, versioning, and traceability to support modularity and consistency across environments and models.

Design, develop, test, and deploy knowledge graph solutions using cloud-native data pipelines.

Model and evolve graph entities and relationships as new data sources are onboarded.

Design, build, and operate services that expose graph and event-store data as tools for consumers, including graph queries, event-store queries, and schema discovery.

Define tool contracts, context, and guardrails to support accurate, grounded responses from AI agents.

Support low-latency, secure, and cost-efficient serving for interactive and batch AI workloads.

Monitor and maintain observability for data pipelines and AI services, including data freshness, pipeline health, query latency and cost, tool-call success rates, and answer quality.

Implement SLOs, dashboards, alerting, and tracing while supporting incident response and continuous reliability improvements.

Partner with data engineers and application data source owners to ingest and validate data.

Establish data contracts, schema validation, and data quality checks.

Support data onboarding, mapping to logical data models, and troubleshooting.

Contribute to data governance, cataloging, and lineage.

7+ years of IT experience.

3+ years of development experience.

2+ years of experience in AI and graph engineering.

Experience with at least one coding language or framework.

Strong software engineering experience with Java and Python.

Experience with production-grade testing, CI/CD, and code quality practices.

Experience deploying data and AI systems to production on a GCP-native stack.

Experience with GCP, Big Query, Python, Java, cloud infrastructure, and artificial intelligence/expert systems.

Experience with cloud technologies including Vertex AI, Big Query, Dataflow/Apache Beam, Pub/Sub, Cloud Run/GKE, Cloud Storage, and Cloud Build/Artifact Registry.

Experience with graph data modeling and querying, including property graphs and GQL/graph query patterns.

Experience with Vertex AI, including agents, model serving, embeddings, and evaluation of agent answer quality.

Experience building LLM/agent systems, including tool use, RAG/grounding, and integrating models through APIs.

Familiarity with MCP or comparable agent tool protocols.

Experience with observability, including Cloud Monitoring/Logging, Open Telemetry, SLOs, dashboards, and alerting for data pipelines and services.

Experience with Infrastructure as Code using Terraform.

Experience with secure-by-default engineering practices, including IAM, least privilege, and secrets management.

Ability to work…