Senior GenAI Application Engineer

1. Design and develop production-grade GenAI applications using Python, Java, or similar backend technologies and modern AI application frameworks.

2. Build agentic workflows using frameworks such as LangGraph, LangChain, or similar orchestration technologies to support multi-step reasoning, tool usage, and workflow automation.

3. Develop and optimize RAG solutions, including document ingestion, chunking, embeddings, retrieval, reranking, context management, and grounded response generation.

4. Integrate LLMs into enterprise applications using hosted LLM APIs, open-weight models, backend services, enterprise APIs, databases, and other organizational data sources.

5. Implement tool calling and prompt orchestration to enable AI applications to interact reliably with enterprise systems, APIs, databases, and operational platforms.

6. Engineer applications for production reliability, including error handling, retries, fallbacks, timeouts, rate limiting, caching, state management, and graceful degradation.

7. Build observability and evaluation capabilities for GenAI applications, including structured logging, tracing, latency monitoring, token usage tracking, quality evaluation, and troubleshooting.

8. Containerize and deploy GenAI applications using technologies such as Docker, Kubernetes, OpenShift, and cloud-based infrastructure.

9. Collaborate across application, data, platform, infrastructure, security, and business teams to design solutions that meet enterprise architecture, security, and operational requirements.

10. Challenge weak technical designs and continuously improve solutions, balancing engineering quality, scalability, maintainability, delivery timelines, and business value without unnecessary over-engineering.

1. 6+ years of software engineering experience, with recent hands-on experience developing Generative AI or AI-powered applications.

2. Proven experience building and supporting production-grade GenAI applications, beyond prototypes, proof-of-concepts, experiments, or demos.

3. Strong hands-on experience with LangGraph, LangChain, or similar GenAI orchestration frameworks, including workflow design and state/context management.

4. Practical experience with RAG, agentic workflows, tool calling, prompt orchestration, context management, and LLM application patterns.

5. Strong backend development skills in Python, Java, or similar programming languages, with an emphasis on clean, maintainable, and testable code.

6. Experience integrating LLMs into real-world applications through APIs, microservices, backend systems, enterprise platforms, databases, and other data sources.

7. Understanding of open-weight models and model-serving patterns, with hands-on experimentation or experience using technologies such as vLLM or similar inference-serving frameworks.

8. Strong understanding of API design, distributed systems, and production resilience patterns, including retries, timeouts, circuit breakers, caching, asynchronous processing, and fault handling.

9. Experience with production observability and deployment, including logging, tracing, monitoring, troubleshooting, Docker, Kubernetes/OpenShift, and cloud or container platforms.

Skills

Back-End Systems, Gentoo, Circuit Breaker, Troubleshooting, Generative AI Application Development and Deployment, Agent Orchestration, Reliability Engineering Management, clean management, Container Orchestration, Inference Engine, Workflow Design, Workflow Orchestration, RAGAS, Corporate Resources, Orchestration, Enterprise Database System Administration