Machine Learning Engineer – MLOps & Cloud Data Engineering

HTC Global Services; Allen Park (MI); Vor Ort; USD 110.000 - 160.000; Vollzeit

Benefits dieser Stelle

Health insurance

401(k) matching

Paid time off

Zusammenfassung

HTC Global Services seeks a Machine Learning Engineer II to design, build, deploy, and scale ML solutions across computer vision, perception, and localization. You will develop scalable pipelines, cloud infrastructure, and production-ready AI systems in collaboration with business and technology stakeholders.

The role emphasizes automating model lifecycle, applying experimental methods, and advancing ML capabilities with a strong focus on MLOps, CI/CD, and reliability in a modern cloud

Qualifikationen

• Bachelor's degree
• 7+ years of IT experience
• 3+ years of development experience
• 2+ years in AI and graph engineering
• Strong software engineering 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 cloud technologies including Vertex AI, BigQuery, Dataflow/Beam, Pub/Sub, Cloud Run/GKE, Cloud Storage, Cloud Build/Artifact Registry
• Experience with graph data modeling and querying (property graphs, GQL)
• Experience with Vertex AI agents, model serving, embeddings, and evaluation of agent answer quality
• Experience building LLM/agent systems (tool use, RAG/grounding, API integrations)
• Familiarity with MCP or comparable agent tool protocols
• Experience with observability (Cloud Monitoring/Logging, OpenTelemetry, SLOs, dashboards, alerting)
• Terraform IaC
• Secure-by-default engineering (IAM, least privilege, secrets management)
• Ability to work with data producers to model enterprise data

Aufgaben

• Collaborate with stakeholders to understand ML requirements
• Design and develop ML models and algorithms for complex business problems
• Design, build, maintain, and optimize scalable ML pipelines, architecture, and infrastructure
• Apply ML and statistical modeling techniques to develop and evaluate algorithms
• Apply ML to VR/AR, object detection, tracking, classification, terrain mapping
• Train and retrain ML models as required
• Deploy ML models into production and run simulations for development and testing
• Automate deployment, training, and retraining using CI/CD/CT and MLOps
• Enable model management and versioning across environments
• Develop knowledge graph solutions using cloud-native pipelines
• Model and evolve graph entities with new data sources
• Expose graph and event-store data via queries for consumers
• Define tool contracts, context, and guardrails for AI agents
• Support low-latency, secure serving for interactive/batch AI workloads
• Monitor observability for data pipelines and AI services
• Implement SLOs, dashboards, alerting, and incident response
• Partner with data engineers to ingest/validate data
• Establish data contracts, schema validation, and data quality checks
• Support data onboarding, mapping to logical models
• Contribute to data governance, cataloging, and lineage

Kenntnisse

Java

Python

CI/CD

GCP

MLOps

Ausbildung

Bachelor's degree

Tools

Vertex AI

BigQuery

Dataflow/Beam

Terraform

OpenTelemetry

Jobbeschreibung

Overview / Summary

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.

Job Description

Machine Learning Engineer II

Overview / Summary

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.

Key Responsibilities

• 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.

Required Qualifications

• Bachelor's degree.
• 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, BigQuery, Python, Java, cloud infrastructure, and artificial intelligence/expert systems.
• Experience with cloud technologies including Vertex AI, BigQuery, 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, OpenTelemetry, 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 directly with data producers to model and validate real-world industrial or enterprise data.

Preferred Qualifications

• Familiarity with Dataplex/Data Catalog for governance, lineage, and business glossaries.
• Experience with streaming/CDC and event-driven architectures.
• Experience with append-only or event-sourced data modeling.
• Experience designing and building user-facing applications and dashboards that surface knowledge graph data.
• Domain exposure to PLM/product development, manufacturing execution, quality, or supply-chain systems and data.
• Experience with data quality frameworks and schema evolution.
• Experience with blue-green or zero-downtime data deployments.

Work Arrangement

• Four days per week onsite.
• Morning work schedule.
• No travel required.

What Makes HTC A Great Place To Build Your Future

HTC Global Services wants you to join our team. Come build new things with us and advance your career. At HTC Global, you’ll collaborate with experts, work alongside clients, and be part of high-performing teams driving success together. You’ll have long-term opportunities to grow your career and develop skills in the latest emerging technologies.

At HTC Global Services, our employees have access to a comprehensive benefits package. Benefits can include Group Health (Medical, Dental, and Vision), Paid Time Off, Paid Holidays, 401(k) matching, Group Life and Disability insurance, Professional Development opportunities, Wellness programs, and a variety of other perks.

Our success as a company is built on inclusion and diversity. HTC Global Services is committed to providing a workplace free from discrimination and harassment, where every employee is treated with dignity and respect. We celebrate differences and believe that diverse cultures, perspectives, and skills drive innovation and success. HTC is an Equal Opportunity Employer and a proud National Minority Supplier. We seek to empower each individual, fostering an environment where everyone feels valued, included, and respected.