Machine Learning Engineer

Stefanini Group•Dearborn, MI

6d•Onsite

About The Position

Stefanini Group is hiring! Stefanini is looking for Machine Learning Engineer, Dearborn, MI For quick apply, please reach out to Lovkesh Bharti at 248-727-2506/[email protected] We are seeking a high-impact AI/ML Engineer to build intelligent data products that turn complex, high-volume engineering information into trusted, actionable insight. You will work across applied machine learning, generative AI, data platforms, and cloud engineering to deliver production systems used for search, traceability, analytics, and decision support. This role is ideal for an engineer who can move from architecture to implementation to operational ownership, and who enjoys solving ambiguous problems where data quality, scale, and reliability matter.

Requirements

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 and evolving datasets.
Hands-on experience applying large language models to real products, including prompt design, structured outputs, tool use, evaluation, and production monitoring.
Strong understanding of embeddings, vector retrieval, RAG architecture, model limitations, and techniques for improving answer quality and faithfulness.
Experience with software engineering fundamentals, including testing, code review, version control, CI/CD, observability, and secure development practices.
Demonstrated ability to diagnose difficult production problems using measurable evidence, experimentation, profiling, and disciplined root-cause analysis.
Experience with workflow orchestration, job scheduling, or reliable batch execution frameworks.
Python
SQL
Artificial Intelligence & Expert Systems
Google Cloud Platform (GCP)
API development and integration
Software testing
Data analysis

Nice To Haves

Experience with agentic AI frameworks, tool-using systems, or multi-step reasoning workflows.
Experience with managed generative AI, model serving, batch inference, or vector database platforms.
Experience with infrastructure-as-code and automated cloud delivery.
Experience extracting meaning from complex documents, legacy formats, technical diagrams, or other semi-structured content at scale.
Experience in automotive, manufacturing, safety-critical, systems engineering, or another technically regulated domain.
Experience building internal analytics products or developer-facing tools that translate complex data into clear decisions.
Data and analytics dashboards
Data collection
Data integrity
Java
Data acquisition
Data conversion

Responsibilities

Architect, build, and operate reliable data products that ingest and transform diverse structured and unstructured information at enterprise scale.
Create resilient orchestration and delivery patterns for batch and near-real-time workloads, with clear observability, alerting, and operational runbooks.
Develop production Retrieval-Augmented Generation (RAG) systems that combine semantic retrieval, structured data, and grounded responses for high-value engineering use cases.
Design agentic AI workflows that decompose complex questions, select the right data sources and tools, validate results, and return explainable answers with citations.
Develop and evaluate embedding, document-understanding, and multimodal inference workflows, balancing quality, latency, scalability, and cost.
Lead cloud architecture, containerization, infrastructure-as-code, and CI/CD practices for secure, repeatable deployment across environments.
Own system reliability from design through production: investigate incidents, profile performance, eliminate failure modes, and improve capacity planning.
Deliver intuitive analytics experiences and decision-support tools that make complex technical data useful to engineers, program teams, and leadership.
Establish data quality, lineage, validation, and governance practices so users can understand where information came from and how much to trust it.
Build incremental, restartable processing with checkpointing and recovery strategies that protect data integrity during long-running or partially failed workloads.