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AI Tech Lead Forensic Structural Engineering

Job in Atlanta, Fulton County, Georgia, 30383, USA
Listing for: TechDigital Group
Full Time position
Listed on 2026-07-13
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Backend Developer, AWS, Full Stack Developer
Salary/Wage Range or Industry Benchmark: 180000 - 230000 USD Yearly USD 180000.00 230000.00 YEAR
Job Description & How to Apply Below
Position: AI Tech Lead for Forensic Structural Engineering

What are the top 3 skills required for this role?
1. GenAI & Spec-First Development — deep expertise in spec-driven workflows (Git Hub Spec Kit or equivalent), AI agent orchestration, and building AI-powered product features end to end.
2. Full Stack Engineering (React / Node / Python) — extensive experience delivering production applications across the entire stack, from modern React UIs to Node.js and Python backends with robust API layers.
3. Cloud & Data (AWS + MongoDB / PostgreSQL) — strong command of AWS infrastructure and both relational and document databases, including schema design, optimisation, and cloud-native deployment patterns.

Job Description/ Responsibilities

• Drive spec-first development practices across teams — leading the authoring of specs, technical plans, and agent-ready task breakdowns using Git Hub Spec Kit or equivalent tooling before any code is written.

• Architect and build full stack web applications using React and modern JavaScript / Type Script frameworks on the frontend, backed by Node.js and Python services.

• Design, develop, and maintain RESTful and GraphQL APIs — ensuring performance, reliability, versioning, and security across all service boundaries.

• Lead cloud architecture and deployment on AWS, leveraging services such as Lambda, EC2, S3, API Gateway, RDS, and Cloud Formation for scalable, resilient systems.

• Integrate and build AI-powered features using LLMs, AI agents, and prompt engineering techniques, translating GenAI capabilities into tangible product value.

• Own data architecture decisions across MongoDB and PostgreSQL, including schema design, indexing strategies, query optimization, and migrations.

• Mentor and technically guide engineers at all levels, conducting code reviews and raising the overall engineering bar across the organization.

• Partner with product, design, and AI/ML teams to define requirements and translate them into well-specified, high-quality software.

• Contribute to engineering strategy, tooling choices, and cross-team standards as a senior technical leader.

Required Qualifications

• 12+ years of professional software engineering experience with a strong full stack background.

• Proven experience with GenAI tools and a spec-first development approach — including Git Hub Spec Kit, AI agent frameworks, or equivalent spec-driven methodologies.

• Expert-level proficiency in React and modern JavaScript / Type Script frameworks (Next.js, Vue, or similar).

• Strong backend development experience with both Node.js and Python — building, maintaining, and scaling production-grade REST and GraphQL APIs.

• Deep, hands-on experience with AWS — comfortable across core services (Lambda, EC2, S3, API Gateway, RDS) as well as security, networking, and cost optimization.

• Solid experience designing and managing both MongoDB (document store) and PostgreSQL (relational) databases at scale.

• Demonstrated ability to integrate LLM APIs (OpenAI, Anthropic, or similar), build prompt engineering pipelines, and deliver AI-augmented product features.

• Track record of leading technical delivery — setting architecture direction, unblocking teams, and owning outcomes across complex, multi-service systems.

• Bachelor's or master's degree in computer science, Engineering, or equivalent practical experience.

Good to Have

• Experience with Git Hub Copilot, Cursor, or AI-assisted development environments integrated into day-to-day engineering workflows.

• Familiarity with containerization (Docker, Kubernetes) and infrastructure-as-code tools (Terraform, AWS CDK).

• Exposure to vector databases (Pinecone, pgvector) or RAG (Retrieval-Augmented Generation) pipeline design.

• Experience with AI orchestration frameworks such as Lang Chain or Llama Index.

• Knowledge of event-driven architecture patterns using AWS SQS, SNS, or Event Bridge.

• Familiarity with MLOps practices and deploying ML models into production pipelines.

• Contributions to open-source projects, technical writing, or a portfolio of AI-integrated applications.

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