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Patterned Learning Ai Jobs in Michigan (NOW HIRING)

Technogen, Inc.

AI Architect

Auburn Hills, MI · On-site

... gateway patterns, and reference architectures consumed by all AI delivery towers and partner ... Machine Learning Lens), and applicable enterprise compliance baselines Agentic AI and LLM ...

VDart

Technical Architect - AI

Detroit, MI · On-site

$60.50 - $73.25/hr

... gateway patterns, and reference architectures consumed by all AI delivery towers and partner ... Machine Learning Lens), and applicable enterprise compliance baselines Agentic AI and LLM ...

X by 2

Technical Lead (Applied AI)

Detroit, MI

AI / Machine Learning Development * Design, develop, and train machine learning and deep learning ... Architect orchestration patterns for planning, task decomposition, memory, context management, and ...

Megan soft Inc

Senior AI/ML Engineer

Dearborn Heights, MI · On-site

$96K - $132K/yr

Artificial Intelligence & Expert Systems, Machine Learning, Data Science, Data Modeling, Software ... Strong technical expertise in cloud services (GCP/Vertex AI) and data integration patterns * Strong ...

Stefanini Group

Machine Learning Engineer

Allen Park, MI · On-site

... patterns/ recommendations from raw data, leveraging data science methodologies including Machine ... Agentic AI Orchestrator : A deployed multi-agent system that autonomously monitors the middleware ...

Stefanini Group

Machine Learning Engineer

Allen Park, MI · On-site

... patterns/ recommendations from raw data, leveraging data science methodologies including Machine ... Agentic AI Orchestrator : A deployed multi-agent system that autonomously monitors the middleware ...

Stefanini

Machine Learning Engineer

Allen Park, MI · On-site

... patterns/ recommendations from raw data, leveraging data science methodologies including Machine ... Agentic AI Orchestrator: A deployed multi-agent system that autonomously monitors the middleware ...

Stefanini Group

Machine Learning Engineer

Allen Park, MI · On-site

... patterns/ recommendations from raw data, leveraging data science methodologies including Machine ... Agentic AI Orchestrator : A deployed multi-agent system that autonomously monitors the middleware ...

Megan soft Inc

AI Engineer (W2 Position)

Dearborn, MI · On-site

$50 - $55/hr

... patterns, make predictions, interpret sensor data (images, sound), orchestrate automation and ... Drive innovative applications of Artificial Intelligence tools and techniques such as deep learning ...

HARMAN International

Senior Engineer, AI

Novi, MI · On-site

$98K - $134K/yr

Artificial Intelligence & Machine Learning Worker Type Reference: Regular - Permanent Pay Rate Type ... patterns over structured and unstructured enterprise data. * Contribute to enterprise data and AI ...

Stellantis

Data & AI SCIENTIST

Auburn Hills, MI · On-site

... trends and patterns * Build predictive models and machine-learning algorithms * Writing and ... Facilitate the internal and external data science & AI network * Be a specialist on specific data ...

Versigent

AI Build Engineer Manager

Troy, MI · On-site

... conceptual patterns into validated, repeatable implementation blueprints. You are a hybrid ... Learning and development opportunities * Discount programs with various manufacturers and retailers

OneStream

AI Engineer

Birmingham, MI · On-site

Our AI solutions incorporate applications across the AI and machine learning spectrum, including ... patterns. * Implement robust AI systems and functionalities, ensuring optimal performance and ...

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Patterned Learning Ai information

What are some typical challenges faced by Patterned Learning AI professionals in implementing AI-driven solutions within organizations?

Patterned Learning AI professionals often encounter challenges such as integrating AI models with existing legacy systems, ensuring high-quality and representative training data, and aligning AI solutions with specific business objectives. Collaboration across multidisciplinary teams—including data scientists, software engineers, and business stakeholders—is essential for successful deployment. Additionally, professionals must stay updated on evolving AI technologies and best practices to maintain model accuracy and address ethical considerations.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need a strong background in mathematics, statistics, programming (especially Python), and a degree in computer science or a related field. Experience with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn, as well as familiarity with cloud computing platforms and data management tools, is essential. Excellent problem-solving skills, creativity, and clear communication are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies are vital for developing reliable AI systems that solve real-world problems and drive innovation.

What is the difference between Patterned Learning Ai vs Data Scientist?

AspectPatterned Learning AiData Scientist
Required CredentialsTypically requires machine learning, AI, or computer science degrees; certifications in AI toolsRequires degrees in statistics, computer science, or related fields; often certifications in data analysis
Work EnvironmentTech companies, AI startups, research labs focusing on AI developmentBusiness, finance, healthcare, and tech sectors analyzing data for insights
Employer & Industry UsageUsed by AI-focused organizations developing intelligent systemsEmployed across industries for data analysis, predictive modeling, and decision support

Patterned Learning Ai primarily focuses on developing AI models and algorithms, often requiring specialized technical skills. Data Scientists analyze data to extract insights and inform business decisions. While both roles involve data and machine learning, Patterned Learning Ai is more centered on creating AI systems, whereas Data Scientists interpret data for strategic purposes.

What is Patterned Learning AI?

Patterned Learning AI refers to artificial intelligence systems designed to recognize, learn from, and replicate patterns in data. These systems use algorithms to identify trends, correlations, and structures within large datasets, enabling them to make predictions or automate decision-making processes. Patterned Learning AI is commonly used in fields like image recognition, natural language processing, and predictive analytics. Its applications help businesses and researchers uncover hidden insights, streamline operations, and improve accuracy in various tasks.
What are popular job titles related to Patterned Learning Ai jobs in Michigan? For Patterned Learning Ai jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Patterned Learning Ai jobs in Michigan look for? The top searched job categories for Patterned Learning Ai jobs in Michigan are:
What cities in Michigan are hiring for Patterned Learning Ai jobs? Cities in Michigan with the most Patterned Learning Ai job openings:
Patterned Learning Ai jobs near you
AI Architect

AI Architect

Technogen, Inc.

Auburn Hills, MI • On-site

Other

Posted 5 days ago

Job description

TECHNOGEN, Inc. is a Proven Leader in providing full IT Services, Software Development and Solutions for 15 years.

TECHNOGEN is a Small & Woman Owned Minority Business with GSA Advantage Certification. We have offices in VA; MD & Offshore development centers in India. We have successfully executed 100+ projects for clients ranging from small business and non-profits to Fortune 50 companies and federal, state and local agencies.


Job Title: AI Architect
Location: Onsite in Auburn Hills, MI
Job Description:
Platform Architecture and Governance
Design the enterprise AI platform architecture spanning the LLM API gateway, GPU and compute allocation pools, sandbox provisioning, model registry, and security gate automation
Define infrastructure standards, API gateway patterns, and reference architectures consumed by all AI delivery towers and partner integrations
Establish guardrails for token metering, rate limiting, audit logging, DLP validation, SAST, DAST, dependency scanning, and model card review embedded in CI/CD
Review security posture across all AI workloads with mapping to NIST AI RMF, AWS Well-Architected (including the Machine Learning Lens), and applicable enterprise compliance baselines
Agentic AI and LLM Engineering
Architect multi-agent systems using LangGraph, LangChain, and Model Context Protocol (MCP) for complex workflow orchestration, planning, and tool use
Define patterns for ReAct, Chain-of-Thought, Tree-of-Thoughts, and agent-to-agent coordination across enterprise and customer-facing use cases
Design and optimize Retrieval-Augmented Generation (RAG) systems, embedding strategies, and semantic search across structured and unstructured enterprise data
Establish MLOps and AgentOps practices for deployment, evaluation, observability, and continuous improvement of agents and models in production
AWS-Native Implementation
Architect solutions on Amazon Bedrock, Amazon SageMaker, Amazon Q, Bedrock Agents, and Bedrock Knowledge Bases
Define infrastructure patterns using Amazon EKS, AWS Lambda, ECS Fargate, API Gateway, EventBridge, SNS/SQS, Kinesis, S3, DynamoDB, Aurora, Redshift, Athena, OpenSearch, and Kendra
Establish CloudFormation and AWS CDK templates and Terraform modules for isolated VPC sandboxes provisioned per project and per third-party partner
Implement observability and FinOps using CloudWatch, AWS Cost Explorer, AWS Budgets, and chargeback reporting by team, project, and model
Salesforce and SaaS AI Integration
Define integration architecture with Salesforce Agentforce, Einstein, Data Cloud, and Service Cloud, including Apex, Flow, and Platform Event integration patterns with AWS-hosted agents and APIs
Establish governance over enterprise SaaS AI licenses, including usage tracking, renewal governance, and redundancy elimination across business units
Architect cross-system identity, authorization, and data exchange patterns spanning Salesforce, AWS, and partner endpoints
Stakeholder and Delivery Leadership
Partner with AIDO leadership, delivery tower leads, security, compliance, procurement, and program management to ensure platform adoption and consistent operating standards
Produce enterprise-grade architecture artifacts, decision records, and operating model documentation suitable
Mentor engineers across delivery towers and partner teams; lead architecture reviews and technical due diligence on partner-built systems
Core AI Frameworks
Expert proficiency with LangGraph, LangChain, and agent orchestration frameworks
Deep experience with Amazon Bedrock, SageMaker, and Amazon Q, including Bedrock Agents and Knowledge Bases
Hands-on experience with Model Context Protocol (MCP), function calling, tool use, and structured output patterns
Strong command of prompt engineering, evaluation harnesses, fine-tuning, and model optimization
Working knowledge of transformer architectures, attention mechanisms, and multi-modal systems
Machine Learning
Classical ML (regression, tree-based ensembles, gradient boosting, clustering) and deep learning (CNNs, RNNs, transformers) across supervised, unsupervised, and reinforcement paradigms; feature engineering, hyperparameter optimization, cross-validation, drift detection, and model evaluation;
end-to-end ML lifecycle on SageMaker spanning data preparation, training, deployment, monitoring, and retraining.
AWS Platform
SageMaker (Studio, Pipelines, Model Registry, Inference), Bedrock, EKS, Lambda, ECS Fargate, API Gateway, Step Functions
S3, DynamoDB, Aurora, Redshift, Athena, OpenSearch, Kendra
EventBridge, SNS/SQS, Kinesis, MSK
CloudWatch, X-Ray, CloudTrail, AWS Config, GuardDuty, Macie, Security Hub
IAM, KMS, PrivateLink, VPC design, and AWS Organizations governance
Salesforce and Enterprise SaaS
Salesforce Agentforce, Einstein, Data Cloud, Service Cloud, and Sales Cloud integration patterns
Apex, Flow, Platform Events, and REST/Bulk API integration with external AI services
Familiarity with enterprise identity providers, SSO, OAuth, and SCIM provisioning across SaaS estates
Programming and Development
Advanced Python with deep FastAPI experience for scalable, async API development
Java proficiency sufficient to integrate with existing enterprise backend services
Strong CI/CD background using AWS CodePipeline, CodeBuild, GitHub Actions, and Infrastructure as Code via Terraform and AWS CDK
Containerization with Docker and orchestration with Kubernetes (EKS)
Data and Vector Systems
Vector store architectures using OpenSearch, Bedrock Knowledge Bases, Pinecone, Weaviate, or Chroma
Embedding model selection, hybrid search, and reranking strategies
Graph database experience (Amazon Neptune, Neo4j) for knowledge representation
Data ingestion, masking, synthetic data generation, and DLP validation pipelines?
Basic Qualifications:
20+ years in software engineering with 5+ years focused on AI/ML systems
3+ years hands-on experience architecting and shipping production LLM and agentic AI applications
Preferred Qualifications:
Demonstrated success leading enterprise-scale AI platform builds with measurable business outcomes
Track record architecting scalable cloud-native systems on AWS in regulated or large-enterprise environments
Experience leading technical teams, mentoring engineers, and engaging executive stakeholders
Education:
Bachelor's or Master's degree in Computer Science, AI/ML, or a related technical field
AWS Certified Solutions Architect Professional or AWS Certified Machine Learning Specialty preferred
Salesforce Certified AI Associate, AI Specialist, or Application Architect credentials is a plus
Akanksha Agarwal | US IT Recruiter

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