Machine Learning Engineer

Stefanini; Dearborn (MI); On-site; USD 68,000 - 108,000; Full time

Job summary

Stefanini Group is hiring a Machine Learning Engineer for a role based in Dearborn, Michigan. You will work across applied machine learning, generative AI, data platforms, and cloud engineering to create production systems for search, traceability, analytics, and decision support.

The position suits an engineer who can take solutions from architecture through implementation and ongoing operations, tackling ambiguous challenges involving data quality, scale, and reliability.

Qualifications

• At least 5 years of experience building and operating production software, data, or machine learning systems.
• Strong Python and SQL skills.
• Professional experience with cloud platforms, managed data services, object storage, containers, and distributed workloads.
• Experience designing and operating scalable data pipelines or distributed processing systems for large, evolving datasets.
• Hands-on experience applying large language models in products, including prompt design, structured outputs, tool use, evaluation, and production monitoring.

Responsibilities

• Architect, build, and operate dependable data products that ingest and transform varied structured and unstructured information at enterprise scale.
• Create resilient orchestration and delivery patterns for batch and near-real-time workloads, supported by observability, alerts, and operational runbooks.
• Develop production RAG systems that combine semantic retrieval, structured data, and grounded responses for engineering use cases.
• Design agentic AI workflows that break down complex questions, choose data sources and tools, validate findings, and provide explainable answers with citations.
• Develop and evaluate embedding, document-understanding, and multimodal inference workflows, balancing quality, speed, scale, and cost.
• Lead cloud architecture, containerization, infrastructure-as-code, and CI/CD practices to enable secure, repeatable deployments.
• Take ownership of system reliability from design through production by investigating incidents, profiling performance, reducing failure modes, and improving capacity planning.
• Build intuitive analytics experiences and decision-support tools for engineers, program teams, and leadership.
• Establish data quality, lineage, validation, and governance practices that help users understand information sources and trustworthiness.
• Implement incremental, restartable processing with checkpointing and recovery strategies to protect data integrity during lengthy or partially failed workloads.

Skills

Python

SQL

Cloud platforms

Data pipelines

LLMs / AI

CI/CD

Observability

Education

Bachelor's degree

Certification program

Tools

Google Cloud Platform

API development

Containerization

Vector databases

Job description

Salary: $68,000 - 108,000 per year

Requirements

• At least 5 years of experience building and operating production software, data, or machine learning systems, with strong Python and SQL skills
• Professional experience with cloud platforms, managed data services, object storage, containers, and distributed workloads
• Experience designing and operating scalable data pipelines or distributed processing systems for large, evolving datasets
• Hands-on experience applying large language models in products, including prompt design, structured outputs, tool use, evaluation, and production monitoring
• Strong understanding of embeddings, vector retrieval, RAG architecture, model limitations, and approaches to improving answer quality and faithfulness
• Knowledge of software engineering practices such as testing, code review, version control, CI/CD, observability, and secure development
• Ability to investigate complex production issues through evidence-based analysis, experimentation, profiling, and root-cause analysis
• Experience with workflow orchestration, job scheduling, or reliable batch execution frameworks
• Bachelors degree required; a certification program is preferred
• Preferred experience with agentic AI frameworks, tool-using systems, or multi-step reasoning workflows
• Preferred experience with managed generative AI, model serving, batch inference, or vector database platforms
• Preferred experience with infrastructure-as-code and automated cloud delivery
• Preferred experience extracting information from complex documents, legacy formats, technical diagrams, or other semi-structured content at scale
• Preferred experience in automotive, manufacturing, safety-critical, systems engineering, or another technically regulated field
• Preferred experience building internal analytics products or developer-facing tools that turn complex data into clear decisions
• Skills include Python, SQL, artificial intelligence and expert systems, Google Cloud Platform, API development and integration, software testing, and data analysis
• Additional preferred skills include analytics dashboards, data collection, data integrity, Java, data acquisition, and data conversion

Responsibilities

• Architect, build, and operate dependable data products that ingest and transform varied structured and unstructured information at enterprise scale
• Create resilient orchestration and delivery patterns for batch and near-real-time workloads, supported by observability, alerts, and operational runbooks
• Develop production RAG systems that combine semantic retrieval, structured data, and grounded responses for engineering use cases
• Design agentic AI workflows that break down complex questions, choose data sources and tools, validate findings, and provide explainable answers with citations
• Develop and evaluate embedding, document-understanding, and multimodal inference workflows, balancing quality, speed, scale, and cost
• Lead cloud architecture, containerization, infrastructure-as-code, and CI/CD practices to enable secure, repeatable deployments
• Take ownership of system reliability from design through production by investigating incidents, profiling performance, reducing failure modes, and improving capacity planning
• Build intuitive analytics experiences and decision-support tools for engineers, program teams, and leadership
• Establish data quality, lineage, validation, and governance practices that help users understand information sources and trustworthiness
• Implement incremental, restartable processing with checkpointing and recovery strategies to protect data integrity during lengthy or partially failed workloads

Technologies

• Agentic AI
• AI
• API
• Architect
• CI/CD
• Cloud
• Support
• Java
• Machine Learning
• Model Serving
• Python
• RAG
• SQL
• GCP

More

We are hiring a Machine Learning Engineer for a role based in Dearborn, Michigan. You will work across applied machine learning, generative AI, data platforms, and cloud engineering to create production systems for search, traceability, analytics, and decision support. The position suits an engineer who can take solutions from architecture through implementation and ongoing operations, tackling ambiguous challenges involving data quality, scale, and reliability. Stefanini Group provides outsourcing, IT digital consulting, systems integration, application, and strategic staffing services to Fortune 1000 organizations. We serve clients across a broad range of industries and maintain a global presence throughout the Americas, Europe, Africa, and Asia. We are a CMM Level 5 IT consulting company.