AI Engineer

Company Overview

Our client is an industry-led, government-supported organization that enables enterprise organizations to access, adopt, and accelerate new digital technologies. Their state-of-the-art physical and digital facilities bring together cutting-edge technology, expertise, and business support to help organizations solve real-world challenges, transform, innovate, and future-proof their operations.

Job Overview

Our client is seeking an AI Engineer to design, build, and deploy practical AI solutions that help organizations solve real operational challenges. The role focuses on applied generative and Agentic AI, including retrieval-augmented generation (RAG), AI assistants, agentic workflows, and multimodal applications that connect securely with enterprise and operational data. Working alongside subject-matter experts and technology partners, you will take use cases from initial discovery and rapid prototyping through to evaluation and production deployment.

Responsibilities

• Applied Generative AI: Build production-ready AI applications including knowledge assistants, copilots, and multimodal solutions. Design RAG solutions over industrial content (SOPs, OCAPs, FMEAs, CAPAs, equipment manuals) producing grounded, traceable responses. Develop agentic workflows using tool calling, structured outputs, and human approval steps.

• AI Engineering & Integration: Develop APIs, services, and reusable components that integrate AI models with enterprise systems, databases, and operational workflows. Evaluate and select models, retrieval approaches, and AI services based on quality, security, latency, cost, and maintainability.

• Production Delivery & Responsible AI: Deploy and operate AI services in cloud platforms using containerisation, version control, and CI/CD practices. Build evaluation and observability into AI solutions, monitoring performance, safety, and user feedback. Apply guardrails, access controls, data-protection practices, and human oversight.

• Operational AI & Collaboration: Work on use cases such as yield analytics, anomaly detection, predictive maintenance, and computer vision, integrating model outputs into usable applications. Collaborate with subject-matter experts to frame use cases, handle structured and unstructured data, and evaluate solutions against operational KPIs. Keep pace with the evolving GenAI/agentic landscape.

Core Tech Stack

• Languages & Core Software: Python, APIs, REST, Testing frameworks, Git / Version Control

• AI, LLM & GenAI: LLM APIs, Open-source LLMs, Retrieval-Augmented Generation (RAG), Vector Databases / Vector Search, Embeddings, Agentic Workflows, Tool Calling, Structured Outputs, Multimodal AI

• Machine Learning & Computer Vision: TensorFlow, PyTorch, scikit-learn, Computer Vision, Edge AI

• Cloud & Infrastructure: AWS, Azure, GCP, Docker, Containerisation, CI/CD pipelines

• Operational Systems & Industrial Data: Enterprise Databases, MES, SCADA, Historians, Sensors, Event Logs, Digital Twins, Simulation

Requirements

• Education & Experience: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related discipline (or equivalent practical experience) alongside 2+ years of relevant experience in AI engineering, software engineering, applied AI, or data science.

• Software & AI Engineering: Strong Python engineering skills with hands-on experience building with LLM APIs, open-source models, embeddings, vector search, structured outputs, and agentic workflows.

• Integration & Cloud: Demonstrated experience integrating AI applications with databases, documents, and APIs, along with working knowledge of AWS, Azure, or GCP, Docker, and CI/CD fundamentals.

• Evaluation & Communication: Proven capability in evaluating and monitoring AI systems for quality, reliability, and safety, paired with the ability to translate technical concepts for non-technical stakeholders.

• Desirable Experience: Exposure to operational, industrial, or IoT data environments (MES, SCADA, historians, sensors, image data), ML/CV frameworks (TensorFlow, PyTorch, scikit-learn), or regulated sector environments (medtech, pharma, food, or discrete production).

Feel Free to Apply Below

If you are a hands-on engineer looking to build state-of-the-art AI applications that drive tangible operational impact, submit your CV to explore this role further with our team.