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Semantic Jobs in Michigan (NOW HIRING)

Kla

AI Software Engineer|Manufacturing

Ann Arbor, MI · On-site

$111K - $188K/yr

Vector database (Milvus) for semantic search, along with a knowledge graph (Neo4j) * Experience orchestrating multi-step AI workflows or agent-based systems * Familiarity with enterprise data ...

Epitec

Business Intelligence Specialist

Dearborn, MI · On-site

$50 - $55/hr

Hands-on Power BI report development, DAX, Power Query, semantic modeling, filters, drilldowns, dashboard UX, performance tuning. Benefits : 80 hours paid time off, medical insurance contributions ...

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All JobsSemantic JobsSemantic Jobs in Michigan

Semantic information

See Michigan salary details

$48.4K

$103.4K

$151.2K

How much do semantic jobs pay per year?

As of Aug 13, 2026, the average yearly pay for semantic in Michigan is $103,436.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,000.00 and $116,400.00 per year, depending on experience, location, and employer.

What is a semantic job?

A Semantic job typically involves working with meaning and context in language, data, or technology. It may include roles in natural language processing (NLP), knowledge representation, search engine optimization (SEO), or semantic web technologies. Professionals in this field develop algorithms, ontologies, and models to improve understanding and classification of information. These jobs are common in AI, data science, and digital marketing industries.

What is the difference between Semantic vs Data Analyst?

AspectSemanticData Analyst
Required CredentialsBackground in linguistics, computer science, or related fields; knowledge of semantic web technologiesDegree in statistics, mathematics, or related fields; proficiency in data analysis tools
Work EnvironmentResearch-focused, often in tech or AI companies, working on language understandingBusiness or research settings, analyzing data to inform decisions
Industry UsageUsed in AI, NLP, and semantic web projectsUsed across finance, marketing, healthcare, and other sectors
Common Search/ComparisonSemantic vs Data Analyst

Semantic professionals focus on understanding and structuring meaning in language and data, often working with AI and NLP technologies. Data Analysts interpret data sets to generate insights for business decisions. While both roles involve data, Semantic roles emphasize language and knowledge representation, whereas Data Analysts focus on statistical analysis and reporting.

What are the key challenges faced by semantic engineers when implementing knowledge graphs in large organizations?

Semantic Engineers often encounter challenges related to integrating disparate data sources, ensuring data quality, and aligning ontologies across departments. In large organizations, there can be legacy systems and inconsistent data formats, making it difficult to create a unified semantic model. Additionally, Semantic Engineers must collaborate closely with data architects, subject matter experts, and software developers to ensure the knowledge graph accurately reflects the organization's information needs and remains scalable as requirements evolve.

What are the key skills and qualifications needed to thrive as a semantic analyst, and why are they important?

To thrive as a Semantic Analyst, you need expertise in linguistics, natural language processing (NLP), data analysis, and a relevant degree such as linguistics, computer science, or information science. Familiarity with tools like Python, NLP libraries (e.g., NLTK, spaCy), and semantic annotation systems is typically required. Strong analytical thinking, attention to detail, and effective communication skills help you interpret complex language data and collaborate with technical teams. These competencies are vital to accurately extract, structure, and apply meaning from language data, driving insights and solutions in various industries.

What are the most commonly searched types of Semantic jobs in Michigan?

The most popular types of Semantic jobs in Michigan are:

What are popular job titles related to Semantic jobs in Michigan?

For Semantic jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Semantic jobs in Michigan look for?

The top searched job categories for Semantic jobs in Michigan are:

Semantic jobs near you
Infographic showing various Semantic job openings in Michigan as of August 2026, with employment types broken down into 83% Full Time, 8% Part Time, 1% Temporary, and 8% Contract. Highlights an 72% Physical, 7% Hybrid, and 21% Remote job distribution, with an average salary of $103,436 per year, or $49.7 per hour.

Agentic AI Engineer -Auburn Hills, MI-2 days onsite : Contract on w2

Marvel Technologies Inc

Auburn Hills, MI • On-site

$58 - $62/hr

Contractor

Re-posted 27 days ago

Job description

Agentic AI/ML Engineer -Hybrid (Only w2 consultants)
Auburn Hills, MI-2 days onsite
12 months
 
Need 10+ plus years experience
 

We are seeking a highly skilled Senior Agentic AI Engineer to join our team and drive the development of cutting-edge autonomous AI systems. This role focuses on building sophisticated AI agents that can reason, plan, and execute complex tasks with minimal human intervention.
 
## Position Summary
As a Senior Agentic AI Engineer, you will design and implement intelligent agent systems using state-of-the-art frameworks and technologies. You'll work at the intersection of large language models, knowledge graphs, and distributed systems to create AI solutions that can autonomously handle complex workflows and decision-making processes.
 
## Key Responsibilities
**Agent Development & Architecture**
- Design and implement multi-agent systems using LangGraph for complex workflow orchestration
- Build autonomous AI agents capable of planning, reasoning, and task execution
- Develop agent communication protocols and coordination mechanisms
- Create robust error handling and recovery systems for agent workflows
 
**AI Infrastructure & Integration**
- Implement and optimize LangChain pipelines for agent reasoning and tool usage
- Design and maintain vector database solutions for knowledge retrieval and semantic search
- Build scalable data ingestion pipelines for structured and unstructured data
- Integrate Google Cloud AI services including Vertex AI, Gemini, and PaLM models
 
**Backend Development & APIs**
- Develop high-performance APIs using FastAPI for agent interaction and monitoring
- Implement real-time streaming capabilities for agent responses and status updates
- Build monitoring and observability systems for agent performance tracking
- Create robust authentication and authorization systems for agent access
**Data & Knowledge Management**
- Design and implement RAG (Retrieval-Augmented Generation) systems
- Optimize vector embeddings and similarity search algorithms
- Build knowledge graph integration for enhanced agent reasoning
- Implement efficient caching and data persistence strategies
 
## Required Technical Skills
**Core AI/ML Frameworks**
- Expert-level proficiency with LangGraph for agent workflow orchestration
- Deep experience with LangChain for LLM application development
- Hands-on experience with Google Cloud AI services (Vertex AI, Gemini, PaLM)
- Strong understanding of prompt engineering and LLM optimization techniques
**Data & Storage**
- Proficiency with vector databases (Pinecone, Weaviate, Chroma, or similar)
- Experience with traditional databases (PostgreSQL, MongoDB)
- Knowledge of data ingestion frameworks and ETL pipelines
- Understanding of embedding models and semantic search optimization
**Python Development**
- Advanced Python programming skills with 10+ years of experience
- Expert-level FastAPI development for building scalable APIs
- Proficiency with async/await patterns and concurrent programming
- Experience with Python ML libraries (NumPy, Pandas, scikit-learn)
- Knowledge of testing frameworks (pytest, unittest) and CI/CD practices
**Cloud & Infrastructure**
- Hands-on experience with Google Cloud Platform (GCP)
- Proficiency with containerization (Docker) and orchestration (Kubernetes)
- Experience with cloud storage solutions and data pipelines
- Understanding of microservices architecture and distributed systems
## Preferred Qualifications
 
**Additional Technical Skills**
- Experience with other agent frameworks (AutoGPT, CrewAI, TaskWeaver)
- Knowledge of graph databases (Neo4j, Amazon Neptune)
- Familiarity with streaming frameworks (Apache Kafka, Redis Streams)
- Experience with monitoring tools (Prometheus, Grafana, LangSmith)
**AI/ML Expertise**
- Understanding of transformer architectures and attention mechanisms
- Experience with fine-tuning and adapting large language models
- Knowledge of reinforcement learning for agent training
- Familiarity with multi-modal AI systems (vision, text, audio)
**Development Experience**
- Experience with frontend frameworks (React, Vue.js) for agent interfaces
- Knowledge of WebSocket programming for real-time applications
- Familiarity with message queues and event-driven architectures
- Experience with performance optimization and scalability challenges