USA_Enterprise Architect

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. 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)||.

Pay Rate Range: $ 64.39 - 68.18/hr.

Enterprise AI Architect (FDE-Oriented)

Job Description:
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.

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 years

 

Skills:	Category Name Required Importance Experience SkillCategoryTest1_MN AI & Gen AI - Products & Tools Yes 1 >7 years   SkillCategoryTest1_MN AI Agents Yes 1 >7 years   SkillCategoryTest1_MN Digital : Google Cloud Yes 1 >7 years