Machine Learning Engineer 3
Machine Learning Engineering Engineer 3
Dearborn, MI
W2
Position Description
We are seeking an experienced AI Engineer to design, develop, and deploy intelligent solutions that leverage Machine Learning, Large Language Models (LLMs), and emerging Agentic AI capabilities to transform business processes and drive operational efficiency. This role combines expertise in Data Science, Software Engineering, and MLOps to deliver scalable, production-ready AI systems that generate measurable business value. The ideal candidate will have hands-on experience building and operationalizing AI/ML solutions in enterprise environments, with a strong focus on Generative AI, intelligent automation, and cloud-native architectures. Key Responsibilities Design, develop, and deploy machine learning models, including predictive, optimization, and Generative AI solutions. Build end-to-end AI workflows encompassing data ingestion, feature engineering, model training, deployment, monitoring, and continuous improvement. Develop and implement LLM-powered applications, including Retrieval-Augmented Generation (RAG), prompt orchestration, agentic workflows, and tool integrations. Create scalable APIs and AI services that seamlessly integrate with enterprise applications and business processes. Establish and maintain MLOps practices, including automated training, deployment, monitoring, retraining, and performance management. Ensure AI solutions are reliable, scalable, secure, and optimized for production environments. Collaborate closely with business and technical stakeholders to identify opportunities and translate business challenges into AI-driven solutions. Monitor model performance and implement ongoing enhancements based on business feedback, operational metrics, and evolving requirements. Stay current with advancements in AI, Generative AI, Agentic AI, and MLOps to continuously improve solution capabilities and delivery approaches. Required Qualifications Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related discipline. Strong programming experience in Python, including backend development, API design, automation, and software engineering best practices. Hands-on experience building, deploying, and supporting machine learning models in production environments. Experience with machine learning frameworks such as Scikit-learn, TensorFlow, and/or PyTorch. Practical experience developing applications using Large Language Models (LLMs), prompt engineering, and Generative AI technologies. Experience building AI solutions on cloud platforms such as GCP and/or AWS. Strong understanding of software development lifecycle, version control, testing, and deployment practices. Excellent analytical, problem-solving, and communication skills. Ability to thrive in a fast-paced, agile environment with evolving priorities and business needs
Skills Required
Python, Machine Learning, Data Science, GCP, Big Query
Experience Required
Engineer 3 Exp: Prac. In 2 coding lang. or adv. Prac. in 1 lang. 6+ years in IT; 4+ years in development Experience designing and implementing Agentic AI solutions, multi-step workflows, autonomous agents, and tool-calling architectures. Experience with AI orchestration frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, or similar technologies. Hands-on experience with MLOps tools and platforms including MLflow, Airflow, Vertex AI, SageMaker, Kubeflow, or equivalent solutions. Experience with containerization and orchestration technologies such as Docker and Kubernetes. Familiarity with vector databases, embeddings, Retrieval-Augmented Generation (RAG), and semantic search architectures. Experience working with enterprise-scale data environments, data lakes, and large datasets. Experience optimizing AI systems for scalability, performance, reliability, and cost efficiency. Experience building AI-powered products, dashboards, analytics solutions, or intelligent automation platforms.
Experience Preferred
Self-starter with the ability to work independently and navigate ambiguity. Strong communicator capable of engaging both technical and non-technical stakeholders. Collaborative team player who can effectively partner across business and technology functions. Innovative thinker with a passion for applying AI to solve real-world business challenges. Results-oriented mindset focused on delivering practical, scalable, and impactful solutions.
Education Required
Bachelor's Degree
Education Preferred
Additional Safety Training/Licensing/Personal Protection Requirements:
Additional Information
4 days in the office Python (advanced), SQL Machine Learning & Deep Learning LLMs, Prompt Engineering, RAG, Embeddings Agentic AI / AI Agents / Tool Calling Vector Databases ML Frameworks: Scikit-learn, TensorFlow, PyTorch MLOps: MLflow, Airflow, CI/CD, model deployment & monitoring Cloud: AWS or GCP Docker, Kubernetes API development (FastAPI / Flask) Data pipelines (ETL), data lakes/warehouses Strong system design & production AI experience
Dearborn, MI
W2
Position Description
We are seeking an experienced AI Engineer to design, develop, and deploy intelligent solutions that leverage Machine Learning, Large Language Models (LLMs), and emerging Agentic AI capabilities to transform business processes and drive operational efficiency. This role combines expertise in Data Science, Software Engineering, and MLOps to deliver scalable, production-ready AI systems that generate measurable business value. The ideal candidate will have hands-on experience building and operationalizing AI/ML solutions in enterprise environments, with a strong focus on Generative AI, intelligent automation, and cloud-native architectures. Key Responsibilities Design, develop, and deploy machine learning models, including predictive, optimization, and Generative AI solutions. Build end-to-end AI workflows encompassing data ingestion, feature engineering, model training, deployment, monitoring, and continuous improvement. Develop and implement LLM-powered applications, including Retrieval-Augmented Generation (RAG), prompt orchestration, agentic workflows, and tool integrations. Create scalable APIs and AI services that seamlessly integrate with enterprise applications and business processes. Establish and maintain MLOps practices, including automated training, deployment, monitoring, retraining, and performance management. Ensure AI solutions are reliable, scalable, secure, and optimized for production environments. Collaborate closely with business and technical stakeholders to identify opportunities and translate business challenges into AI-driven solutions. Monitor model performance and implement ongoing enhancements based on business feedback, operational metrics, and evolving requirements. Stay current with advancements in AI, Generative AI, Agentic AI, and MLOps to continuously improve solution capabilities and delivery approaches. Required Qualifications Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related discipline. Strong programming experience in Python, including backend development, API design, automation, and software engineering best practices. Hands-on experience building, deploying, and supporting machine learning models in production environments. Experience with machine learning frameworks such as Scikit-learn, TensorFlow, and/or PyTorch. Practical experience developing applications using Large Language Models (LLMs), prompt engineering, and Generative AI technologies. Experience building AI solutions on cloud platforms such as GCP and/or AWS. Strong understanding of software development lifecycle, version control, testing, and deployment practices. Excellent analytical, problem-solving, and communication skills. Ability to thrive in a fast-paced, agile environment with evolving priorities and business needs
Skills Required
Python, Machine Learning, Data Science, GCP, Big Query
Experience Required
Engineer 3 Exp: Prac. In 2 coding lang. or adv. Prac. in 1 lang. 6+ years in IT; 4+ years in development Experience designing and implementing Agentic AI solutions, multi-step workflows, autonomous agents, and tool-calling architectures. Experience with AI orchestration frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, or similar technologies. Hands-on experience with MLOps tools and platforms including MLflow, Airflow, Vertex AI, SageMaker, Kubeflow, or equivalent solutions. Experience with containerization and orchestration technologies such as Docker and Kubernetes. Familiarity with vector databases, embeddings, Retrieval-Augmented Generation (RAG), and semantic search architectures. Experience working with enterprise-scale data environments, data lakes, and large datasets. Experience optimizing AI systems for scalability, performance, reliability, and cost efficiency. Experience building AI-powered products, dashboards, analytics solutions, or intelligent automation platforms.
Experience Preferred
Self-starter with the ability to work independently and navigate ambiguity. Strong communicator capable of engaging both technical and non-technical stakeholders. Collaborative team player who can effectively partner across business and technology functions. Innovative thinker with a passion for applying AI to solve real-world business challenges. Results-oriented mindset focused on delivering practical, scalable, and impactful solutions.
Education Required
Bachelor's Degree
Education Preferred
Additional Safety Training/Licensing/Personal Protection Requirements:
Additional Information
4 days in the office Python (advanced), SQL Machine Learning & Deep Learning LLMs, Prompt Engineering, RAG, Embeddings Agentic AI / AI Agents / Tool Calling Vector Databases ML Frameworks: Scikit-learn, TensorFlow, PyTorch MLOps: MLflow, Airflow, CI/CD, model deployment & monitoring Cloud: AWS or GCP Docker, Kubernetes API development (FastAPI / Flask) Data pipelines (ETL), data lakes/warehouses Strong system design & production AI experience
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