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Ai System Architect Jobs in Indiana (NOW HIRING)

Patrick Industries

IT Sr. Analyst, AI Engineer

Elkhart, IN · On-site

$120 - $160/hr

... System Architecture & Engineering * Design system components, service boundaries, data flows ... Build integrations between AI platforms and enterprise applications, including ERP, operational ...

Elevance Health

Cloud & AI Security Architect

Indianapolis, IN · Hybrid

$62.50 - $83.25/hr

Cloud & AI Security Architect Cloud & AI Security Architect Location: This role requires associates ... in systems administration and security aspects of information systems, access management and ...

Elevance Health

Cloud & AI Security Architect

Indianapolis, IN · On-site

$62.50 - $83.25/hr

Cloud & AI Security Architect Cloud & AI Security Architect Location: This role requires associates ... in systems administration and security aspects of information systems, access management and ...

Elevance Health

Cloud & AI Security Architect

Indianapolis, IN · Hybrid

$62.50 - $83.25/hr

Cloud & AI Security Architect Location: This role requires associates to be in-office 1-2 days per ... in systems administration and security aspects of information systems, access management and ...

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Ai System Architect information

What is an AI system architect?

AI System Architects are professionals who design, plan, and oversee the development of artificial intelligence systems within organizations. They are responsible for creating the overall architecture for AI solutions, ensuring that different technologies, algorithms, and data sources work together seamlessly. AI System Architects collaborate with data scientists, engineers, and business stakeholders to implement scalable and effective AI-driven products and services. Their role often includes evaluating new technologies, setting best practices, and ensuring the security and scalability of AI systems.

How does an AI system architect typically collaborate with data scientists and engineers during a project?

An AI System Architect works closely with data scientists to understand the requirements and constraints of AI models and ensures that system infrastructure supports their needs. They collaborate with engineers to design scalable, robust architectures and oversee the integration of AI components into existing systems. Regular communication and joint problem-solving are essential, as architects bridge the gap between high-level AI goals and practical implementation, often facilitating technical discussions and making critical design decisions.

What are the key skills and qualifications needed to thrive as an AI system architect, and why are they important?

To thrive as an AI System Architect, you need a deep understanding of machine learning, distributed systems, data engineering, and typically a degree in computer science or a related field. Familiarity with cloud platforms (like AWS, GCP, or Azure), AI frameworks (such as TensorFlow or PyTorch), and experience with system design are essential, along with relevant certifications. Strong problem-solving skills, strategic thinking, and effective communication set top candidates apart. These skills ensure the architect can design scalable, efficient, and robust AI solutions that align with business objectives and technical requirements.

What is the difference between Ai System Architect vs Data Scientist?

AspectAi System ArchitectData Scientist
Required CredentialsBachelor's or master's in CS, AI, or related fields; certifications in AI/MLBachelor's or master's in CS, Statistics, or related fields; certifications in data analysis or ML
Work EnvironmentDesigning AI systems, collaborating with engineers, focusing on architectureAnalyzing data, building models, interpreting results
Employer & Industry UsageTech companies, AI-focused firms, R&D departmentsTech, finance, healthcare, marketing, research organizations

While both roles require knowledge of AI and ML, an Ai System Architect primarily designs and oversees AI system architecture, ensuring integration and scalability. In contrast, a Data Scientist focuses on analyzing data, building models, and deriving insights. The roles often collaborate but differ in their core responsibilities and focus areas.

How do you become an AI system architect?

To become an AI system architect, one typically needs a strong background in computer science, software engineering, or related fields, along with expertise in artificial intelligence, machine learning, and system design. Gaining experience through relevant projects, obtaining certifications in AI or cloud platforms, and developing skills in programming languages like Python and tools such as TensorFlow or PyTorch are also important steps.

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What job categories do people searching Ai System Architect jobs in Indiana look for?

The top searched job categories for Ai System Architect jobs in Indiana are:

What cities in Indiana are hiring for Ai System Architect jobs?

Cities in Indiana with the most Ai System Architect job openings:

Ai System Architect jobs near you
Infographic showing various Ai System Architect job openings in Indiana as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 14% Part Time, and 3% Contract. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution.

AI Enabled Automation Architect (pharmaceutical manufacturing)

Vailexa Technology LLC

Indianapolis, IN • On-site

Other

Posted 16 days ago

Job description

Role- AI Enabled Automation Architect
Location- Indianapolis, IN onsite
Duration - 18 months
 
Required Qualifications
  1. Experience with tech transfer, manufacturing automation, recipe design, control strategies, and batch execution workflows; specifically batch process experience, not continuous process control.
  2. Minimum 6 years of hands-on, batch-specific DeltaV configuration experience (not continuous), plus working knowledge of MES, data integration, and digital manufacturing architectures.
  3. Pharmaceutical or life sciences manufacturing experience; Small Molecule experience specifically preferred, with biologics, advanced therapies, or multi-modality experience considered a plus.
  4. Capability in data modeling, information architecture, knowledge representation, or related digital-knowledge disciplines.
  5. Experience bridging process knowledge and digital execution systems without over-reliance on custom application development.
  6. Experience designing modular, reusable technical frameworks rather than one-off implementations.
  7. Hands-on experience with AI or machine learning tools in an regulated technical environment, with the aptitude and willingness to grow into more advanced AI-driven workflows (e.g. agentic architectures).
  8. Ability to work iteratively using real process examples, user feedback, and rapid learning cycles.
  9. Strong cross-functional engagement skills with automation, process engineering, operations, PT&E, quality, and related stakeholder groups.
Preferred Capabilities
  • AI-enabled analysis, decision support, or knowledge-system experience in regulated technical environments; particularly agentic architecture and coding experience (e.g. Claude Code, Codex).
  • Change-management, training, and governance experience to help operationalize adoption across functions.
  • Familiarity with data integrity principles (ALCOA+) and data governance frameworks.
  • Experience with Kneat, ValGenesis, or similar electronic validation management systems.

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