Role Overview
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.
Key Responsibilities
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.
Qualifications & Experience
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.
