Artificial intelligence is reshaping every industry, and expertise in Retrieval‑Augmented Generation (RAG) and large language model (LLM) agents is in sky‑high demand. Companies are racing to build AI‑powered assistants that can seamlessly interact with existing software, creating a premium market for senior engineers who can bridge research and production. This Senior AI Engineer contract offers a chance to lead cutting‑edge AI agent development while leveraging deep Java and Python experience.

Job Summary

We are seeking a seasoned Senior AI Engineer to design, develop, and deploy Retrieval‑Augmented Generation pipelines, LLM‑based agents, and AI‑driven assistants. The role requires 20+ years of hands‑on experience, deep proficiency in Java and Python, and familiarity with agentic frameworks such as ADK, LangChain, and LangGraph. You will also build and consume RESTful APIs to enable seamless interaction between AI assistants and enterprise software.

Top 3 Critical Skills Table

Interview Preparation

1. Explain how you would design a RAG pipeline that integrates a proprietary knowledge base with an LLM.
2. What the interviewer is looking for: Understanding of vector stores, embedding generation, retrieval strategies, and prompt engineering.
3. Walk through the steps to build an AI agent using LangChain that can invoke external RESTful services.
4. What the interviewer is looking for: Familiarity with LangChain agents, tool‑calling patterns, and API authentication handling.
5. Describe performance optimization techniques for a Java‑based microservice that serves LLM inference requests.
6. What the interviewer is looking for: Knowledge of concurrency, caching, batching, and resource‑efficient model serving.
7. How do you ensure data privacy and compliance when your AI assistant accesses sensitive enterprise data via APIs?
8. What the interviewer is looking for: Awareness of encryption, tokenization, access controls, and audit logging.
9. Compare the trade‑offs between using a hosted LLM service versus self‑hosting an open‑source model for an AI agent platform.
10. What the interviewer is looking for: Insight into cost, latency, scalability, customization, and security considerations.

Resume Optimization

• Retrieval‑Augmented Generation (RAG)
• Large Language Model (LLM) development
• AI Agent frameworks (LangChain, LangGraph, ADK)
• Java backend development
• Python scripting and automation
• RESTful API design & consumption
• Enterprise software integration
• C2C contract experience
• 20+ years of AI/ML engineering
• Agentic architecture implementation

Application Strategy

When reaching out to the recruiter, send a concise email that starts with a friendly greeting, attach your updated resume, and clearly highlight your most relevant expertise. Mention your extensive work with RAG, LLM‑based agents, and Java/Python microservices, and reference any recent projects where you built AI assistants that consumed REST APIs. Emphasize that you are a local candidate in Alpharetta and can start immediately.

Career Roadmap