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Artificial Intelligence Analyst Jobs in Michigan

IBM

Data is the foundation of Artificial Intelligence and digital transformation. During your ... Analyze structured and unstructured data to identify trends, generate insights, and support ...

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Artificial Intelligence Analyst information

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$35.7K

$87.2K

$134.7K

How much do artificial intelligence analyst jobs pay per year?

As of Sep 8, 2026, the average yearly pay for artificial intelligence analyst in Michigan is $87,210.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,100.00 and $105,000.00 per year, depending on experience, location, and employer.

What does an artificial intelligence analyst do?

An Artificial Intelligence Analyst evaluates AI technologies, analyzes data trends, and helps develop AI-driven solutions for businesses. They work with machine learning models, data sets, and automation tools to optimize processes and improve decision-making. Their role often involves collaborating with data scientists, engineers, and business teams to implement AI strategies.

What does a typical workday look like for an artificial intelligence analyst?

A typical workday for an Artificial Intelligence Analyst involves collecting and preparing datasets, developing and testing machine learning models, and analyzing results to improve accuracy and performance. You may collaborate closely with data scientists, engineers, and business stakeholders to align AI initiatives with organizational goals. Additionally, frequent meetings to discuss findings, present data-driven insights, and refine strategies are common. Balancing technical tasks with effective communication ensures that AI solutions are both actionable and valuable to the company.

What are the key skills and qualifications needed to thrive as an artificial intelligence analyst?

An Artificial Intelligence Analyst typically requires a strong background in computer science, mathematics, and statistics, often supported by a relevant degree or certification. Familiarity with programming languages such as Python or R, experience with machine learning frameworks like TensorFlow or PyTorch, and proficiency in data visualization tools are commonly expected. Strong analytical thinking, problem-solving abilities, and effective communication skills set exceptional candidates apart. These skills ensure the analyst can develop, interpret, and clearly explain complex AI models to drive real-world business solutions.

How can I become an artificial intelligence analyst?

To become an artificial intelligence analyst, you typically need a bachelor's degree in computer science, data science, or a related field, along with strong programming skills in languages like Python or R. Gaining experience with machine learning frameworks, data analysis, and statistical methods is essential, and earning certifications in AI or data science can enhance your qualifications. Developing a solid understanding of algorithms, data management, and problem-solving skills is also important for success in this role.

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Artificial Intelligence Analyst jobs near you
Infographic showing various Artificial Intelligence Analyst job openings in Michigan as of August 2026, with employment types broken down into 89% Full Time, and 11% Contract. Highlights an 100% In-person job distribution, with an average salary of $87,210 per year, or $41.9 per hour.

Artificial Intelligence Senior Associate

IPS Technology Services

Dearborn, MI • Hybrid

$50 - $55/hr

Full-time

Posted 7 days ago

Job description

Job Title: Artificial Intelligence Senior Associate
Location: Dearborn, MI (local preferred)
Duration: 12 Months (with potential for extension)
Interview: Interview onsite in South Lyon/Novi, MI

Candidate must be USC or GC. Do not apply for C2C. It will be a hybrid position. REVIEW JD MAKE SURE REQUIRED SKILLS (highlighted red) ARE ON THE RESUME. Will be onsite 4 days a week.

Position Description:
Employees in this job function are responsible for developing intelligent programs, cognitive applications and algorithms for data analysis and automation, leveraging various AI techniques such as deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized programming

Key Responsibilities:
  • Understand business requirements and develop AI algorithms, models and programs to solve complex problems, generate recommendations, extract patterns, make predictions, interpret sensor data (images, sound), orchestrate automation and enable self-service capabilities
  • Perform large-scale experimentation and develop data driven applications that translate data into actionable intelligence
  • Drive innovative applications of Artificial Intelligence tools and techniques such as deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized programming
  • Research and optimize AI technologies to enhance efficiency and accuracy of data analysis and create more efficient automation

Skills Required:
  • Google Cloud Platform

Experience Required:
  • Bachelor's or Master's degree in Computer Science, Software Engineering, or related field (or equivalent practical experience).
  • 3+ years building production software systems, including 1–2+ years on ML/AI or LLM-based applications. Proven experience designing and deploying multi-agent or multi-service architectures in production — not just notebooks or demos.
  • As one 2026 hiring analysis puts it, the job is closer to distributed systems engineering with a probabilistic component than it is to ML research or prompt tweaking .
  • Strong Python proficiency, including async/concurrent programming, and experience with backend frameworks (FastAPI, Flask).
  • Hands-on experience with agent orchestration frameworks — LangGraph, CrewAI, LlamaIndex, or equivalent — for building stateful, multi-step, tool-using agent workflows.
  • Practical experience building RAG pipelines: vector databases (pgvector, Pinecone, Weaviate, or Qdrant), embeddings, chunking strategies, and retrieval evaluation. Cloud deployment experience, ideally Google Cloud Platform (BigQuery, Cloud Run/GKE, Vertex AI, Pub/Sub) or equivalent AWS/Azure services.
  • Strong SQL skills and experience with cloud data warehouses. Containerization and CI/CD experience (Docker, Kubernetes, GitHub Actions/Cloud Build).
  • Experience building evaluation and observability pipelines for LLM/agent systems — offline eval sets, LLM-as-judge scoring, and tracing tools (LangSmith, Langfuse, OpenTelemetry, or equivalent) to track task success, latency, and cost.
  • Understanding of LLM safety practices: guardrails, output validation, prompt-injection defense, and safe execution of AI-generated code/SQL (sandboxing, least privilege). Solid software engineering fundamentals: API design, testing, version control, security best practices.

Experience Preferred:
  • Experience with cost optimization and model routing — designing tiered pipelines that route between low-cost and high-capability models based on task complexity, and modeling per-conversation or per-task cost at scale.
  • Experience deploying agentic systems with human-in-the-loop or multi-checkpoint validation workflows for high-reliability/high-stakes use cases.
  • Experience with automotive, EV charging, IoT, or connected-vehicle telemetry data.
  • Familiarity with Model Context Protocol (MCP) or similar standards for tool/data integration across agents.
  • Prior experience in a startup or 0-to-1 product environment, comfortable with ambiguity and fast-evolving requirements.
Education Required:
  • Bachelor's Degree

Education Preferred:
  • Master's Degree

Additional Information:
  • Architect and deploy the production multi-agent orchestration layer (interpreter/orchestrator, NL-to-SQL agent, visualization agent, RCA/RAG agent, report composition agent, notification agent), using modern agent frameworks with state management and checkpointing rather than ad-hoc loops.
  • Design and productionize RAG pipelines (chunking, embeddings, hybrid retrieval, reranking) grounded in approved schemas, engineering documentation, and historical issue records.
  • Own BigQuery integration and enforce safe, least-privilege, validated execution of LLM-generated SQL. Build CI/CD, containerization, and infrastructure-as-code for deploying agent services on GCP (Cloud Run/GKE, Vertex AI).
  • Implement evaluation pipelines and observability/tracing for every agent (golden datasets, LLM-as-judge scoring, regression alerts) so quality is measurable, not assumed.
  • Implement guardrails, prompt-injection defenses, and human-in-the-loop approval checkpoints to ensure correctness and safety before any output triggers downstream action. Design cost/latency optimization strategies, including tiered model routing (cheap filter models vs. high-capability deep-dive models) and caching.
  • Integrate validated outputs with operational systems (Salesforce ticketing, driver/site-manager notifications) and report export pipelines (PDF/HTML/spreadsheet).
  • Collaborate with data scientists to productionize prototypes (anomaly detection, diagnostic agents) into scalable, monitored services.
  • Establish versioning, testing, and safe rollout practices (canary/shadow deployments) for evolving agent logic.


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About IPS Technology Services

Sourced by ZipRecruiter

In today's Tech driven world, Businesses and Organizations need to stay on the cutting edge to thrive and outshine the competition. We at IPS Technology Services fully understand the need today's business has for a full spectrum of IT services delivered in a Transparent, Cost effective way. Our dedicated team has many combined years of experience in several tech sectors, allowing us to offer a vast array of services, including IT staffing, CIO advisory, Digital marketing, Systems development and both Healthcare and engineering IT. In addition, we even offer IT outsourcing services to clients who need operation support for older systems that they still rely on. We will never force you to abandon the system that works for your needs–at IPS Technology Services, we fully understand that each company is different and there is no one-size fits all solution.

Company size

11 - 50 Employees

Headquarters location

Troy, MI, US

Year founded

2004

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