Description

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

Key Skills

Education

Bachelor's degree