Description

Job Description

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:

• Static Application Security Testing (SAST)

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• Software Composition Analysis (SCA)

• Container Security Scanning

• Dependency Management

• Policy Compliance Validation

• 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:

• Enterprise Architect Agent

• Solution Architect Agent

• Data Architect Agent

• Backend Engineering Agent

• Test Engineering Agent

• Security Review Agent

• 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:

• Visual Studio Code with GitHub Copilot

• Claude Code

• OpenAI Codex

• 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:

• Databricks Lakehouse

• Databricks Genie

• Delta Lake

• ML/AI Pipelines

• Snowflake Cortex

• Enterprise Data Governance

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• Enable self-service analytics, conversational AI, semantic data access, and enterprise-scale data engineering

capabilities.

Preferred FDE-Focused Skills

Must-Have

Enterprise Architecture • Solution Architecture • Full Stack Development • AI-Assisted Software Engineering • GitHub

Copilot • Claude Code • Codex • Agentic AI Frameworks • Cloud Architecture (Azure/AWS/GCP) • Modern CI/CD •

DevSecOps • Microservices Architecture • API Design • Platform Engineering • AI Engineering

Security & Compliance

OWASP • HIPAA • PHI/PII Controls • SAST • SCA • Container Security • Zero Trust Architecture • Secure SDLC

Observability & Operations

OpenTelemetry • Distributed Tracing • Monitoring & Alerting • Application Performance Management • Reliability

Engineering • Production Readiness Reviews

Data & AI Platforms

Databricks • Databricks Genie • Snowflake Cortex • Lakehouse Architecture • Data Governance • AI/ML Platforms

Key Skills

Education

Any Graduate