AI is reshaping how software is built, and companies are racing to embed intelligent agents into every step of the development pipeline. This AI Engineer – Agentic SDLC Automation role puts you at the heart of that transformation, letting you design multi‑agent systems that write, test, and deploy code automatically. If you have a solid engineering background and hands‑on AI experience, this contract opportunity in Atlanta offers a fast‑track to showcase cutting‑edge skills.

Job Summary

Design, develop, and deploy agentic AI solutions that automate the entire Software Development Lifecycle. Leverage LangChain, LangGraph, or CrewAI to build multi‑agent systems for code generation, testing, incident analysis, and CI/CD optimization. Collaborate with DevOps, cloud, and tool integration teams (GitHub, Jenkins, Jira, ServiceNow) to deliver end‑to‑end automation.

Top 3 Critical Skills Table

Interview Preparation

1. How would you design a multi‑agent system to generate, test, and deploy a microservice?
   What the interviewer is looking for: Understanding of agent coordination, prompt engineering, and integration with CI/CD pipelines.
2. Explain the differences between Pinecone and FAISS for vector similarity search and when you’d choose each.
   What the interviewer is looking for: Depth of knowledge in vector databases, scalability, and cost considerations.
3. Walk us through a recent project where you integrated LLMs with a tool like Jira or ServiceNow.
   What the interviewer is looking for: Practical experience in tool integration, API usage, and handling real‑world data.
4. What are the security implications of deploying LLM‑driven code generators in production, and how would you mitigate them?
   What the interviewer is looking for: Awareness of prompt injection, code safety, sandboxing, and compliance.
5. Describe how you would implement Retrieval‑Augmented Generation (RAG) for incident analysis within an SDLC context.
   What the interviewer is looking for: Ability to combine external knowledge bases with LLMs to produce accurate, context‑aware outputs.

Resume Optimization

• AI Engineer
• Agentic AI
• LangChain
• LangGraph
• CrewAI
• LLM
• Retrieval‑Augmented Generation (RAG)
• Pinecone
• FAISS
• CI/CD Automation
• AWS / Azure / GCP
• Microservices
• Python / Java / Go
• GitHub Integration
• Jenkins
• Jira
• ServiceNow

Application Strategy

When reaching out to the recruiter, send a concise email that starts with a friendly greeting, attaches your resume, and clearly maps your experience to the role. Highlight your top skills—such as Agentic AI, LangChain orchestration, and cloud DevOps—and reference specific projects where you built end‑to‑end automation pipelines. Emphasize your 5‑10 years of software engineering plus 2+ years of AI/ML work, and mention any relevant certifications or open‑source contributions.

Career Roadmap