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Job Purpose
At tiQtoQ, we’re passionate about redefining engineering and AI automation. As part of our continuous evolution, we’re looking for an AI Engineer to join our team.
You will develop robust, scalable, and intelligent agentic systems that integrate seamlessly into software development lifecycles (SDLC). You’ll lead the charge in developing LLM-powered agents, assistants, custom tooling for developers, and AI automation frameworks.
This is a hands-on role that combines software engineering, A experimentation, and AI product innovation. You’ll be empowered to shape agent architecture, explore cutting-edge frameworks like LangChain and LlamaIndex, and be responsible for operationalising high-performance, real-time AI systems.
Key Responsibilities
* LLM Agent Development: Design and build intelligent, multi-step LLM agents using frameworks like LangChain, LangGraph, LlamaIndex or similar to support engineering workflows.
* AI Assistant & Tool Innovation: Build AI-powered agents, assistants, and tools that streamline workflows and enhance user productivity. Have a deep understanding of how these systems function under the hood and contribute to their development, scalability, reliability, and continuous improvement.
* AI Engineering: Build, test, and optimise generative AI features using OpenAI, Claude, Gemini, and fine-tuned custom models. Develop model evaluation pipelines and optimise model behaviour.
* Prompt Engineering: Craft, iterate, and test prompt strategies and templates to improve accuracy, robustness, and responsiveness across different LLM backends.
* Cross-Functional Collaboration: Partner with engineering, product, and QA teams to deliver agent-led automation solutions embedded within developer workflows and CI/CD pipelines.
* Prototyping & R&D: Drive rapid prototyping of LLM-based solutions, develop PoCs, and validate ideas using real-world data, user feedback, and experimentation.
* Responsible AI: Promote safe, ethical, and transparent AI practices. Stay vigilant against risks such as prompt injection, data leakage, and model drift.
* AI Advocacy: Lead workshops, demos, and documentation efforts to evangelise AI agent capabilities across the organisation and clients.
Why You're Made for This
* Agentic Experience: Hands-on experience building agent workflows using frameworks like LangChain, LangGraph, or similar orchestration engines.
* Deep AI Knowledge: Strong foundation in AI/ML concepts, especially around LLMs, prompt engineering, embeddings, and vector search.
* Engineering Excellence: Fluency in Python (AI workflows) and TypeScript (full-stack product integration). Able to write robust, testable production code.
* Product Thinking: Keen product sensibility with the ability to translate user needs into functional AI experiences.
* Cloud & Data Skills: Strong exposure to cloud platforms (Azure/AWS/GCP).
* Model Deployment: Experience evaluating, fine-tuning, and deploying LLMs in production, including latency/throughput trade-offs and monitoring strategies.
* Collaborative Spirit: Able to work with engineers, product managers, and QA specialists in a highly cross-functional and fast-paced environment.
* Experimentation Mindset: Passion for testing ideas, building rapidly, and learning from experiments in a structured way.
Nice to Have
* Experience designing RAG (Retrieval-Augmented Generation)/ CAG systems using vector databases and hybrid search strategies.
* Knowledge of LLM vulnerabilities, including adversarial prompting and data poisoning.
* Experience in designing prompt evaluation frameworks using metrics like truthfulness, helpfulness, and toxicity.
* Hands-on experience with LLM agents, building dev tools (ideally like Copilot, Cursor, and others). You need to know how they work under the hood and how to scale them.
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