We are seeking a hands-on Senior AI Engineer to design, build, and deliver production-grade AI solutions. You will build core capabilities for our AI Agent platform, engineer advanced retrieval mechanisms (RAG/GraphRAG), and develop secure microservices and APIs. Working closely with business stakeholders, product leads, and senior architects, you will bridge the gap between cutting-edge LLM architectures and production-ready enterprise applications.

• AI Agent & Solution Development: Design and build production-ready LLM solutions, agentic workflows, task orchestration routines, and automated tools using Python and modern orchestration frameworks.

• RAG & Knowledge Engineering: Implement and optimize enterprise Knowledge Augmentation solutions, leveraging RAG, GraphRAG, vector embeddings, and Knowledge Graph integrations.

• Backend & API Engineering: Develop secure, scalable backend services, APIs, data pipelines, and user-facing features using Python.

• AI-Native Workflow Integration: Utilize and integrate AI coding tools (e.g., Cursor, Claude Code, Copilot) to streamline testing, documentation, and technical solution generation.

• Runtime Quality & Governance: Implement evaluation protocols, logging, telemetry, access control, and guardrails to ensure AI models and agents perform accurately and securely in production.

• Stakeholder & Team Collaboration: Partner with business analysts, QA, security, and risk teams to gather requirements, review code, and ensure solutions meet strict enterprise guidelines.

• Production Support: Support application deployment, real-time monitoring, troubleshooting, and iterative system performance enhancements.

• Education: Bachelor's degree or above in Computer Science, Software Engineering, AI, Data Science, or equivalent practical experience.

• Professional Experience: 1+ years in software engineering or AI engineering, with a proven track record of shipping scalable enterprise backend services or automation platforms.

• Core Programming Skills: High proficiency in Python, plus working knowledge of an additional enterprise language ecosystem (e.g., Java, TypeScript/JavaScript, or Go).

• AI & LLM Experience:

  • Hands-on expertise building LLM applications, prompt orchestration, function calling, memory, and evaluation frameworks.

  • Practical experience using LangChain, LangGraph, or equivalent LLM orchestration frameworks.

  • Direct experience implementing enterprise RAG, vector databases, or GraphRAG.

• Engineering Best Practices: Solid understanding of microservices, RESTful APIs, database design, distributed systems, and security/privacy controls.