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Enterprise AI Architect

Q1 Technologies, Inc

  • Aurora, IL
  • 1 day ago

    Highlights

    Role Descriptions: AI Architect| Exp 10 or moreMust Have| Strong understanding of AIML concepts supervisedunsupervised learning| deep learning| NLP| computer vision| LLMsExperience with ML frameworks TensorFlow| PyTorch| Scikit-learnHands-on experience with MLOps tools and practicesStrong data engineering knowledge (ETL| data lakes| streaming)API design and microservices architectureProficiency in Python or similar languagesCloud Architecture| Expertise in AzureExperience designing distributed and scalable systemsKnowledge of containers and orchestration (Docker| Kubernetes)||Roles Responsibilities||Design end-to-end AIML solutions aligned with business objectivesDefine AI system architectures including data pipelines| model lifecycle| APIs| and deployment strategiesEvaluate and select appropriate AIML models| frameworks| and platformsEnsure scalability| performance| security| and reliability of AI systemsCollaborate with data scientists to operationalize models (MLOps)Guide teams on model training| validation| deployment| monitoring| and retrainingDesign data architectures for structured and unstructured dataEstablish best practices for AI governance| explainability| and bias mitigation. Essential Skills: AI Architect| Exp 10 or moreMust Have| Strong understanding of AIML concepts supervisedunsupervised learning| deep learning| NLP| computer vision| LLMsExperience with ML frameworks TensorFlow| PyTorch| Scikit-learnHands-on experience with MLOps tools and practicesStrong data engineering knowledge (ETL| data lakes| streaming)API design and microservices architectureProficiency in Python or similar languagesCloud Architecture| Expertise in AzureExperience designing distributed and scalable systemsKnowledge of containers and orchestration (Docker| Kubernetes)||Roles Responsibilities||Design end-to-end AIML solutions aligned with business objectivesDefine AI system architectures including data pipelines| model lifecycle| APIs| and deployment strategiesEvaluate and select appropriate AIML models| frameworks| and platformsEnsure scalability| performance| security| and reliability of AI systemsCollaborate with data scientists to operationalize models (MLOps)Guide teams on model training| validation| deployment| monitoring| and retrainingDesign data architectures for structured and unstructured dataEstablish best practices for AI governance| explainability| and bias mitigation.
    Q1 Technologies, Inc

    Numbers & Facts

    LocationAurora, IL
    IndustryComputer/IT Services
    Company Size500 to 999 employees
    Year Founded1990
    Websitehttp://q1tech.com/

    Description

    :**Please strictly adhere to the following resume naming convention:


    ALL CAPS, NO SPACES B/T UNDERSCORES

    PTN_US_GBAMSREQID_CandidateBeelineID

    i.e. PTN_US_9999999_SKIPJOHNSON0413

    MSP Owner: William Bristol
    Location: See Below
    Duration: 6 months


    Enterprise AI Architect (FDE-Oriented)

    Enterprise AI Architect with Full Development Experience (FDE), possessing deep expertise in architecture, hands-on software engineering, AI-assisted development, Agentic AI frameworks, DevSecOps, platform engineering, cloud-native solutions, and enterprise data platforms. Proven ability to architect, develop, secure, automate, and operationalize large-scale AI and software solutions while driving engineering excellence through GitHub Copilot, Claude Code, Codex, Databricks Genie, Snowflake Cortex, and modern AI-powered software delivery practices.

    Key Responsibilities

    1. Enterprise AI & Solution Architecture

    · Lead the architecture, design, and implementation of enterprise-scale AI solutions using modern architectural patterns, clean architecture principles, domain-driven design (DDD), and cloud-native technologies.

    · Define enterprise AI reference architectures, engineering standards, development frameworks, and implementation guardrails to ensure scalability, maintainability, security, and operational excellence.

    · Drive adoption of Agentic AI, AI-powered software engineering, and intelligent automation across the software delivery lifecycle.

    · Architect solutions with built-in observability, resilience, governance, security, and compliance from inception through production deployment.

    · Partner with business, engineering, security, and platform teams to align AI capabilities with enterprise technology strategy and business outcomes.

    2. Full Development Experience (FDE) and Engineering Excellence

    · Demonstrate hands-on full-stack development experience spanning frontend, backend, APIs, data platforms, cloud services, and AI-enabled applications.

    · Lead development teams in implementing modern engineering practices including test-driven development (TDD), CI/CD automation, code quality enforcement, and platform engineering standards.

    · Define and enforce software engineering best practices with mandatory automated test coverage, code reviews, architecture reviews, and deployment quality controls.

    · Drive modernization of legacy applications through refactoring, cloud migration, microservices transformation, and AI-assisted development methodologies.

    · Establish engineering productivity frameworks leveraging AI coding assistants, automated development workflows, and intelligent code generation.

    3. Secure-by-Design AI Platforms

    · Architect secure AI and software platforms aligned with OWASP standards, Zero Trust principles, and enterprise cybersecurity requirements.

    · Implement enterprise controls for HIPAA, PHI, PII, GDPR, and regulatory compliance across data, applications, and AI workloads.

    · Integrate security validation throughout the development lifecycle using SAST, SCA, container scanning, secrets management, and policy-as-code frameworks.

    · Design auditable AI systems with governance, lineage, traceability, access controls, and compliance monitoring capabilities.

    4. AI Engineering, DevSecOps, and Delivery Automation

    · Design and implement AI Engineering Harnesses supporting build validation, quality gates, security scanning, automated testing, and deployment automation.

    · Establish enterprise DevSecOps frameworks integrating:

    o Static Application Security Testing (SAST)

    o Software Composition Analysis (SCA)

    o Container Security Scanning

    o Dependency Management

    o Policy Compliance Validation

    o Infrastructure-as-Code Governance

    · Lead implementation of performance benchmarking frameworks for APIs, AI models, applications, and distributed platforms.

    · Build highly automated CI/CD pipelines enabling secure, reliable, and repeatable software delivery.

    5. Agentic AI Development Frameworks

    · Design and operationalize multi-agent software engineering ecosystems to accelerate architecture, development, testing, security review, and governance activities.

    · Utilize specialized AI agents including:

    o Enterprise Architect Agent

    o Solution Architect Agent

    o Data Architect Agent

    o Backend Engineering Agent

    o Test Engineering Agent

    o Security Review Agent

    o Pull Request Review Agent

    · Drive adoption of agent-based development workflows to improve engineering productivity, software quality, and delivery velocity.

    6. AI-Assisted Software Engineering Toolchain

    · Extensive hands-on experience using:

    o Visual Studio Code with GitHub Copilot

    o Claude Code

    o OpenAI Codex

    o Enterprise AI coding assistants

    · Leverage repository-wide reasoning, large-scale codebase analysis, architecture discovery, code modernization, and AI-assisted implementation patterns.

    · Architect AI-powered developer experiences integrating intelligent code review, automated remediation, documentation generation, and engineering workflow automation.

    7. Data & AI Platform Architecture

    · Design and implement scalable data and AI platforms leveraging Databricks, Snowflake, cloud-native services, and modern data architectures.

    · Experience with:

    o Databricks Lakehouse

    o Databricks Genie

    o Delta Lake

    o ML/AI Pipelines

    o Snowflake Cortex/CoCo

    o Enterprise Data Governance

    · Enable self-service analytics, conversational AI, semantic data access, and enterprise-scale data engineering capabilities.

    Preferred FDE-Focused Skills

    Must-Ha


    Role Descriptions: AI Architect| Exp 10 or moreMust Have| Strong understanding of AIML concepts supervisedunsupervised learning| deep learning| NLP| computer vision| LLMsExperience with ML frameworks TensorFlow| PyTorch| Scikit-learnHands-on experience with MLOps tools and practicesStrong data engineering knowledge (ETL| data lakes| streaming)API design and microservices architectureProficiency in Python or similar languagesCloud Architecture| Expertise in AzureExperience designing distributed and scalable systemsKnowledge of containers and orchestration (Docker| Kubernetes)||Roles Responsibilities||Design end-to-end AIML solutions aligned with business objectivesDefine AI system architectures including data pipelines| model lifecycle| APIs| and deployment strategiesEvaluate and select appropriate AIML models| frameworks| and platformsEnsure scalability| performance| security| and reliability of AI systemsCollaborate with data scientists to operationalize models (MLOps)Guide teams on model training| validation| deployment| monitoring| and retrainingDesign data architectures for structured and unstructured dataEstablish best practices for AI governance| explainability| and bias mitigation
    Essential Skills: AI Architect| Exp 10 or moreMust Have| Strong understanding of AIML concepts supervisedunsupervised learning| deep learning| NLP| computer vision| LLMsExperience with ML frameworks TensorFlow| PyTorch| Scikit-learnHands-on experience with MLOps tools and practicesStrong data engineering knowledge (ETL| data lakes| streaming)API design and microservices architectureProficiency in Python or similar languagesCloud Architecture| Expertise in AzureExperience designing distributed and scalable systemsKnowledge of containers and orchestration (Docker| Kubernetes)||Roles Responsibilities||Design end-to-end AIML solutions aligned with business objectivesDefine AI system architectures including data pipelines| model lifecycle| APIs| and deployment strategiesEvaluate and select appropriate AIML models| frameworks| and platformsEnsure scalability| performance| security| and reliability of AI systemsCollaborate with data scientists to operationalize models (MLOps)Guide teams on model training| validation| deployment| monitoring| and retrainingDesign data architectures for structured and unstructured dataEstablish best practices for AI governance| explainability| and bias mitigation
    Desirable Skills: Google Cloud Architect
    Keyword: ~Google Cloud Architect~
    Skills: Digital : Google Cloud~AI and Automation~AI Agents~AI & Gen AI - Products & Tools
    Experience Required: 8-10

    About Company

    Q1 consists of experienced and recognized experts providing the capability to respond to market demand in order to provide professional services for our clients including Enterprise software implementations, application integration and technical / functional support.

    Q1 has steadily grown into a Quality IT services and solutions organization with the average experience of our team being over 10 years. We have continuously met or exceeded client expectations by delivering professional services and project implementations on time and under budget to help clients truly recognize return on investment.

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