Lead Software Engineer - AI Application Platform :: Charlotte, North Carolina (Hybrid, Need only Locals with DL Copy)

Job Title Lead Software Engineer - AI Application Platform

Location Charlotte, North Carolina (Hybrid, Need only Locals with DL Copy)

Duration 3 Year Contract

Mode of interview 1 round in person

Job Description :-

Main Skill set: Python, AI and Angular

We are seeking a Lead Software Engineer to guide the architectural development and execution of AppGen, a sophisticated AI-powered application generation platform. This role suits a proven technical leader with deep, hands-on expertise across the full software stack who finds enabling a team to build better software deeply satisfying.

You will shape critical systems, mentor senior and junior developers through complex technical decisions, conduct rigorous code reviews across multiple technology domains, and directly influence the platform's trajectory through strategic engineering leadership.

This is for someone who:

• Engages thoughtfully when a junior developer asks targeted architectural questions-because you see an opportunity to shape how someone thinks about systems
• Takes time to explain subtle type-safety issues in code review, understanding that feedback is a teaching moment
• Can present architecture clearly to executives and confidently explain both what we're building and why it matters
• Finds more energy in the code your team ships than in the code you write individually
• Has proven depth across the full stack and a track record of developing engineers into stronger contributors

• If that describes you, we'd like to talk.

About AppGen: Platform Complexity You'll Lead

AppGen is an enterprise-grade, multi-tenant LLM-driven application generator deployed across:

• Frontend Layer: Angular 19 SPA with NgRx state management, Material Design, and real-time WebSocket communication
• Backend Services: Python/FastAPI microservices handling form generation, data persistence, and orchestration
• AI Integration: AWS Bedrock (Claude 3.5) token-optimized prompt chains generating production-grade form schemas and auxiliary code
• Infrastructure: Terraform-managed AWS environments (VPC, Lambda, RDS PostgreSQL, DynamoDB, API Gateway, ALB, AppSync)
• Deployment Automation: NestJS Lambda orchestrating GitHub repo creation, CI/CD pipeline automation, and infrastructure provisioning

This is not a single-language codebase. The role requires the ability to make informed decisions on TypeScript design patterns, Python FastAPI architecture, AWS security posture, and Terraform state management in context with one another.

The Platform Challenge

AppGen is fundamentally a Platform-as-a-Service (PaaS) for dynamic application generation. This differs from building a traditional SaaS product. Rather than building one application, you're building infrastructure that enables users to build their own applications.

What this means architecturally:

• Dynamic Content Generation at Scale: Unlike traditional development where code is fixed, AppGen generates JSON form schemas, validation rules, and UI layouts on demand. The FormBuilder component doesn't know what fields will exist until runtime. The layout engine renders user-designed screens from configuration, not hardcoded templates.
• Multi-Tenant Isolation & Data Segregation: Each user gets their own generated app, potentially deployed to their own AWS environment. The architecture must account for data isolation, namespace management, and cross-tenant security considerations.
• User-Defined Data Structures: Traditional applications are built with predetermined database schemas. AppGen works differently-form structures, field types, and validation rules emerge from user conversations with Claude. This brings engineering challenges: How do you safely execute validation logic that users define? When users modify existing forms that have thousands of submissions, how do you maintain backward compatibility? How do you version schemas?
• Content Rendering, Not Code Generation: Unlike traditional no-code platforms where users drag-and-drop to build, AppGen uses AI instead. Users chat with Claude, Claude generates a form schema, and your platform renders that schema reliably across diverse field types, validation patterns, and workflows. The system renders configurations for immediate use, rather than generating code for later deployment.

Experience that directly transfers:

• You've contributed to or led development of low-code/no-code platforms (visual builders, workflow engines, configuration-driven systems)
• You've worked on SaaS platforms with multi-tenant architecture and understand isolation strategies, rate limiting, and per-customer customization
• You've built dynamic rendering systems that handle unknown/arbitrary schemas at runtime
• You've addressed the unique challenges of treating data configurations as user-created content (form builders, report designers, automation workflows)
• You understand the difference between platform infrastructure and applications built on that infrastructure-and the architectural implications of each

Core Responsibilities

1. Technical Architecture & Systems Thinking (40%)

• Shape architectural decisions across the full stack: How should the component layer handle dynamically generated forms? What's the right approach to validate complex cross-field dependencies in the FormBuilder? What separation of concerns makes sense between the Generator Lambda and the Parent Backend?
• Guide architecture discussions: Help senior developers think through design trade-offs. Should we use NgRx or Angular signals for this feature? When does a new Lambda function become worthwhile given cold-start costs?
• Identify and address system-wide bottlenecks: Work across layers to improve performance. Explore Lambda cold-start optimization, RDS query efficiency, and DynamoDB access patterns.
• Establish patterns and guide consistency: Define coding conventions that work across Python, TypeScript, and Terraform. Help new team members understand the reasoning behind architectural choices.

• What this looks like in practice: You're able to justify architectural decisions with technical reasoning. When someone questions an approach, you can explain the trade-offs you considered. You can write code in multiple languages to validate an approach if needed.

1. Code Review & Technical Guidance (30%)

• Full-stack PR reviews: Review Python FastAPI endpoints and Angular components with equal depth, understanding how they interact.
• Deep technical review: Catch issues thoughtful code review can surface:
  • RxJS Observable lifecycle and potential memory patterns in Angular
  • Query efficiency and data loading patterns in SQLAlchemy
  • Terraform module organization and state management implications
  • Type safety and TypeScript coverage gaps
  • AWS security and IAM configurations
• Educational feedback: Your code reviews help the team learn. When you identify an issue, reviewees understand not just what changed, but how to think about similar problems in the future.
• Define quality expectations: Work with the team to establish what "production-ready" means for this platform and support consistent application of those standards.

• What this requires: Experience reviewing code across teams and multiple languages. You know how to write feedback that resonates-clear, constructive, and focused on helping people improve.

1. Mentorship & Team Development (20%)

• Expand specialist capabilities: Help backend specialists learn to contribute to the forms-engine. Support frontend experts in understanding FastAPI patterns.
• Accelerate junior developers: Pair on complex problems. Explain the reasoning behind patterns like DataState. Connect architectural choices to implementation details and performance implications.
• Identify and address gaps: Recognize when someone is struggling with a technology and provide targeted support-training, pair programming, or guidance through architectural decisions.
• Create growth opportunities: Stretch the team into new areas. A backend engineer working on their first Terraform contribution. A frontend specialist implementing an AWS Lambda authorizer.

• What this requires: Genuine investment in people's growth. You've walked developers through major transitions (generalist to specialist, specialist to full-stack, or into new technology areas). You understand that team strength grows when individuals expand their capabilities.

1. Stakeholder Communication & Technical Leadership (10%)

• Explain to diverse audiences: Translate architectural choices and trade-offs for product managers, executives, and business stakeholders. Connect "optimizing DynamoDB queries" to "improving form submission latency by 30%."
• Shape technical direction: Contribute the engineering perspective on feasibility, risk, and what unlocks future capabilities.
• Support release confidence: You understand the code changes, comprehend the risks, and know what to monitor. You can stand behind releases.

Akshit Sisonia - Sr. Technical Recruiter

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