About the Role

We're looking for an AI Engineer who can take ideas from prototype to production — building scalable AI/ML systems and the data architecture behind them. This is a hands-on role combining strong software engineering, applied machine learning, Generative AI and data engineering.

The ideal candidate will be responsible for developing practical AI solutions that support business transformation, automation and improved decision-making across AGMC.

What You'll Do

Design, build and optimize ML/DL models and LLM-powered applications, including Agentic AI, Generative AI and RAG solutions.
Build end-to-end AI pipelines covering training, inference, evaluation and monitoring.
Develop clean, scalable and well-tested backend services and APIs using FastAPI / Flask.
Design data architecture and manage data storage, primarily using PostgreSQL, including schema design, data modelling and performance optimization.
Work with large datasets, including data preprocessing, feature engineering and model evaluation.
Develop and integrate AI solutions into existing business applications and systems.
Monitor AI/ML models in production and continuously improve accuracy, latency, reliability and scalability.
Develop proof-of-concepts and take successful AI solutions through to production.
Collaborate with IT, Digital, Product, Data and business teams to identify and deliver AI use cases.
Ensure AI solutions follow appropriate security, privacy, governance and responsible AI practices.
Stay up to date with emerging AI technologies and identify opportunities to apply them within AGMC.

What We're Looking For

Strong Python skills and solid software engineering fundamentals, including clean code, testing and Git.
Hands-on experience with PyTorch and/or TensorFlow.
Practical experience with Agentic AI, Generative AI, LLMs and RAG.
Experience with LLM frameworks and tooling such as LangChain and/or LlamaIndex.
Strong data architecture and database skills, particularly PostgreSQL.
Familiarity with vector databases such as Pinecone, Weaviate or FAISS.
Experience with cloud platforms such as AWS, Microsoft Azure or Google Cloud Platform.
Understanding of MLOps and production deployment, including Docker, Kubernetes, CI/CD and MLflow.
Experience developing and consuming REST APIs.
Strong analytical and problem-solving skills.
Clear communicator with the ability to work independently and take ownership of projects from concept through to production.
Experience working in an Agile / collaborative environment.

Nice to Have

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering or a related field.
Experience deploying AI applications at scale within an enterprise environment.
Strong prompt engineering skills and understanding of AI safety and responsible AI.
Experience with AI agents and multi-agent architectures.
Exposure to reinforcement learning or multimodal AI.
Experience working with automotive, retail, mobility or other customer-focused industries would be an advantage.
Experience taking AI solutions from proof-of-concept to production.

Tools & Technologies

Python · FastAPI / Flask · PyTorch / TensorFlow · PostgreSQL · SQL · LangChain / LlamaIndex · RAG · Vector Databases · Docker · Kubernetes · Git · AWS · Azure · GCP · MLflow · CI/CD

Artificial Intelligence Engineer