Machine Learning Engineering Senior Engineer

Machine Learning Engineering Senior Engineer

Fulltime Onsite position

Location: Detroit, MI

Employees in this job function are responsible for designing, building, deploying, and scaling complex self-running ML solutions — including Generative AI and Large Language Model (LLM) systems — in areas such as computer vision, perception, localization, natural language processing, and conversational AI.

They automate and optimize the end-to-end ML and Gen AI model lifecycle using expertise in experimental methodologies, statistics, prompt engineering, and coding for tool building and analysis.

Design and develop innovative ML models, Gen AI systems, and software algorithms — including LLM-based architectures (e.g., transformer models, RAG pipelines, fine-tuned foundation models) — to solve complex business problems in both structured and unstructured environments.

Skills Required:

GCP, Big Data, Artificial Intelligence & Expert Systems, API

1. GCP – Experience deploying and managing services on Google Cloud Platform, including Compute Engine, Cloud Storage, IAM, and Cloud Functions.
2. For example, designing and implementing a cloud-native application architecture using GKE (Google Kubernetes Engine) with Cloud SQL and Pub/Sub.
3. Big Data – Experience working with large-scale data processing frameworks such as Apache Spark, Dataflow, or BigQuery.
4. For example, building ETL pipelines that process terabytes of daily event data and transform it for downstream analytics.
5. Data Warehousing – Experience designing and maintaining data warehouse solutions (e.g., BigQuery, Snowflake, Redshift).
6. For example, modeling a star schema for a retail analytics platform that supports reporting on sales, inventory, and customer behavior.
7. Artificial Intelligence & Expert Systems – Experience developing or integrating AI/ML models and rule-based expert systems.
8. For example, building a classification model using Vertex AI to predict customer churn, or implementing a rule engine that automates underwriting decisions.
9. API – Experience designing, building, and consuming RESTful or gRPC APIs.
10. For example, developing a versioned REST API with OAuth 2.0 authentication that serves as the integration layer between a mobile application and backend microservices.

Skills Preferred:

Google Cloud Platform

1. Google Cloud Platform – Familiarity with advanced GCP services beyond core compute and storage, such as Vertex AI, Dataflow, Cloud Composer (Airflow), and BigQuery ML.
2. For example, using Cloud Composer to orchestrate scheduled data pipelines that feed into a BigQuery data warehouse.

Experience Required:

Senior Engineer Exp: Prac. In 2 coding lang. or adv. Prac. in 1 lang.; guides.

10 years in IT; 8 years in development

Experience Preferred:

• Strong understanding of Generative AI principles and architectures, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems.
• Proven experience in building and deploying RAG systems, including the use of Vector Databases.
• Proficiency in Python programming.
• Solid experience with SQL for data manipulation and querying.
• Hands-on experience with Google Cloud Platform (GCP) services relevant to AI/ML.
• Basic understanding and practical experience with Machine Learning model fine-tuning.
• Familiarity with data engineering concepts and practices.
• Expertise in prompt engineering techniques for interacting with LLMs.
• Experience with the OpenAI SDK.
• Experience developing robust APIs, preferably with FastAPI.
• Proficiency with version control systems (e.g., Git).
• Experience with containerization technologies (e.g., Docker).

Education Required:

Bachelor's Degree

Education Preferred:

Certification Program

Additional Safety Training/Licensing/Personal Protection Requirements:

Additional Information:

POSITION IS HYBRID 3 TO 4 DAYS PER WEEK IN THE OFFICE

1. Design, build, maintain, and optimize scalable ML and Gen AI pipelines, architecture, and infrastructure, including vector databases, embedding stores, and LLM serving layers
2. Use machine learning and statistical modeling techniques such as decision trees, logistic regression, Bayesian analysis, and deep learning methods, alongside prompt engineering, retrieval-augmented generation (RAG), and parameter-efficient fine-tuning (PEFT/LoRA) to develop and evaluate algorithms that improve product/system performance, quality, data management, and accuracy
3. Adapt machine learning and Gen AI capabilities to domains such as virtual reality, augmented reality, object detection, tracking, classification, terrain mapping, intelligent document processing, and AI-powered agent workflows
4. Train, fine-tune, and re-train ML models and LLMs as required, including supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and instruction tuning
5. Deploy ML models, LLMs, and AI agents into production; run simulations and evaluations (including LLM evals and red-teaming) for algorithm development and test various scenarios
6. Automate model deployment, training, re-training, and Gen AI pipeline orchestration, leveraging principles of agile methodology, CI/CD/CT, MLOps, and LLMOps — including guardrail integration, prompt versioning, and observability tooling
7. Enable model management for model versioning, traceability, and governance — including responsible AI practices, bias evaluation, hallucination mitigation, and content safety controls — to ensure modularity and consistency across environments for both ML and Gen AI systems