AI Lead Engineer

Responsibilities / Experience:

• Lead service rationalization and decomposition across complex enterprise ecosystems
• Design and build API-first, event-driven architectures
• Contribute hands-on to domain services, integrations, and POCs
• Enable service reuse through catalogs, standards, and governance
• Drive incremental legacy modernization (strangler pattern)
• Partner across global teams to influence a platform-first mindset
• Build systems ready for AI agents, automation workflows, and future integrations

Qualifications/Required Skills:

1. Strong Engineering & Architecture Execution (10+ years)

• Deep hands-on experience building and delivering production-grade systems in enterprise environments
• Expertise in microservices, APIs (REST/GraphQL), event-driven architecture (Kafka), and cloud platforms (AWS/Azure/Google Cloud Platform)
• Proven ability to move between architecture design, hands-on coding, and leading engineering teams with a pragmatic, delivery-focused mindset

2. Service Decomposition & Enterprise Modernization

• Strong experience with domain-driven design (DDD), service decomposition, and distributed system design
• Hands-on track record, breaking down monoliths/fragmented systems into reusable service layers
• Experience leading modernization efforts using incremental approaches (e.g., strangler pattern) with a focus on reuse, scalability, and clean service boundaries

3. MCP Servers & Gen AI / Agent-Based Systems

• Proven experience building MCP server-based solutions and Gen AI agents from concept through production
• Strong understanding of designing systems for an agentic, AI-driven ecosystem
• Ability to integrate AI into service architectures and make platforms “agent-ready” for future automation and intelligence

4. Experience designing observability strategies: distributed tracing, structured logging, metrics dashboards 

5. Deep understanding of zero-trust architecture, API security, and identity federation

6. Hands-on experience with CI/CD pipeline design, GitOps workflows, and release engineering

7. Ability to make and communicate well-reasoned architectural trade-offs.

Preferred Skills:

• Expertise with Claude Code or similar AI-assisted development tools
• Experience building service catalogs / internal developer platforms
• Background in highly distributed, multi-region enterprise environments
• Exposure to AI-driven automation workflows at scale