We are looking for an innovative and highly skilled AI Application Engineer to join our team and develop state of-the-art enterprise-scale, public-facing AI applications. This role will provide you with the opportunity to shape the next generation of autonomous agent-based systems, focusing on performance, scalability, and cutting-edge technologies such as LLMs, embedding techniques, and agentic frameworks.

As an AI Application Engineer, you will play a critical role in designing and implementing solutions that delight users while optimizing system performance. This position requires a combination of deep technical aptitude, creativity, and a commitment to excellence in application engineering.

Requirements

• Design, develop, and deploy enterprise-scale, public-facing AI applications.
• Implement advanced Retrieval Augmented Generation (RAG) architectures, including hybrid search and multi-vector retrieval. • Build and optimize systems for token usage, response caching, and performance tuning.
• Develop and maintain autonomous agent frameworks using LangGraph or similar framework.
• Drive innovation in areas like embedding techniques, contextual compression, and multi-agent systems.
• Collaborate with cross-functional teams, including product managers, designers, and DevOps, to ensure robust and user-centric solutions.
• Troubleshoot complex technical challenges and mentor junior developers.
• Stay updated on the latest advancements in LLMs, agentic frameworks, and AI-driven application development.

Minimum Qualifications:
• 2–3 years of hands-on experience developing production-grade LLM applications.
• 3 to 5 years of overall software development experience.
• Minimum 1 year of experience in autonomous agent development using the Lang Graph framework or similar.

Must-Have Skills:
1. Production-Grade LLM Development: Proven experience in developing and deploying large language model applications.
2. Autonomous Agent Development: Hands-on experience with frameworks like Lang Graph for building autonomous agents. 3. Advanced RAG Architectures: Expertise in hybrid search, multi-vector retrieval, and contextual compression.
4. Prompt Engineering and Vector Search Optimization: Proficiency in crafting effective prompts and optimizing vector searches.
5. Performance Tuning and Token Optimization: Strong understanding of response caching and techniques to optimize token usage.

Nice-to-Have Skills:
1. Scalable Architectures: Experience with Kubernetes, Docker, and cloud platforms, preferably Azure.
2. NoSQL Databases: Familiarity with MongoDB and Elasticsearch.
3. API-Driven Development: Knowledge of building and maintaining APIs.
4. CI/CD Pipelines and Agile Methodologies: Experience with continuous integration/continuous deployment pipelines and Agile practices.
5. Analytical and Problem-Solving Skills: Strong analytical abilities and attention to detail.