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), CICD 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 CICD 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 MLAI Pipelines
o Snowflake CortexCoCo
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 ResponsibilitiesDesign 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 ResponsibilitiesDesign 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 CloudAI and AutomationAI AgentsAI & Gen AI - Products & Tools
Experience Required: 8-10
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