Senior GenAI Application Engineer

Job Description We are looking for a Senior GenAI Application Engineer to design, build, and deliver production-grade Generative AI applications for enterprise use cases. The ideal candidate will work at the intersection of software engineering, GenAI application development, enterprise integration, data, and platform engineering. This is a hands-on engineering role requiring practical experience with LangGraph, LangChain, RAG, agentic workflows, tool calling, LLM integration, APIs, and enterprise systems. The successful candidate will have strong engineering judgment and a pragmatic approach to building AI solutions that are reliable, observable, maintainable, secure, and scalable. Key Responsibilities

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

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

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

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

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

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

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

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

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

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

Key Requirements

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

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

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

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

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

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

• 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.

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

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