Senior ML Engineer: MLOps & Cloud AI Pipelines

HTC Global; Allen Park (MI); Hybrid; USD 120,000 - 170,000; Full time

Benefits offered by this job

Hybrid and Workplace flexibility

Career development opportunities

Rewards & Recognition program

Job summary

HTC Global Services is seeking a Machine Learning Engineer II to design, build, deploy, and scale ML solutions across computer vision, perception, and localization domains. You will automate the model lifecycle, leverage MLOps, and collaborate with stakeholders to deliver production-ready AI systems on a hybrid work model.

The role emphasizes scalable pipelines, cloud infrastructure, and observability, with a focus on developing robust ML solutions that meet real-world industrial data challenges.

Qualifications

• 7+ years of IT experience.
• 3+ years of development experience.
• 2+ years of AI and graph engineering experience.
• Strong Java and Python development background.
• Production-grade testing, CI/CD, and code quality practices.
• Experience deploying data and AI systems on GCP native stack.
• Hands-on with Vertex AI, BigQuery, cloud infra and AI/expert systems.

Responsibilities

• Collaborate with stakeholders to define ML requirements.
• Design and develop ML models and algorithms for complex business problems.
• Build and optimize scalable ML pipelines, architecture and infra.
• Apply ML and statistical methods for model evaluation.
• Develop ML in areas like computer vision, AR/VR, object detection and terrain mapping.
• Train, retrain, and deploy models into production with CI/CD/CT and MLOps.
• Enable model management, versioning, and traceability across environments.
• Design and deploy knowledge graphs using cloud-native data pipelines.

Skills

Java

Python

CI/CD

GCP

AI/Graph engineering

Observability

Tools

Terraform

Vertex AI

BigQuery

Dataflow/Apache Beam

Pub/Sub

Cloud Run/GKE

Cloud Storage

Cloud Build/Artifact Registry

OpenTelemetry

Job description

HTC Global Services is seeking a Machine Learning Engineer II to design, build, deploy, and scale ML solutions across computer vision, perception, and localization domains. You will automate the model lifecycle, leverage MLOps, and collaborate with stakeholders to deliver production-ready AI systems on a hybrid work model.

The role emphasizes scalable pipelines, cloud infrastructure, and observability, with a focus on developing robust ML solutions that meet real-world industrial data challenges.