Applied AI Engineer

Key Responsibilities

• Work closely with clients to understand business challenges, define AI use cases, and guide them toward the most appropriate solutions across machine learning and generative AI. Translate technical concepts into clear, actionable recommendations and support clients throughout the delivery lifecycle.
• Design, build, and deploy AI systems, including predictive models, NLP solutions, and generative AI applications such as LLM-based solutions, retrieval-augmented generation pipelines, and agentic workflows.
• Work with large structured and unstructured datasets to prepare, clean, and transform data for both traditional machine learning and generative AI use cases.
• Implement and maintain pipelines for deploying, monitoring, and managing the lifecycle of machine learning models and AI applications using cloud-native tools and frameworks.
• Collaborate with data engineers, data scientists, and cross-functional teams to deliver end-to-end AI solutions, from proof-of-concept and MVP through to production.
• Stay up to date with emerging AI and generative AI technologies, actively pursuing continuous learning, certifications, and hands-on experimentation to enhance delivery capability. Recommend improvements to tools, frameworks, and delivery approaches.

Skills, Knowledge and Expertise

• Degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
• Experience developing and deploying machine learning and/or generative AI solutions in production environments.
• Experience delivering end-to-end AI solutions, from proof-of-concept and MVP through to production
• Experience working with cloud platforms (e.g. Azure, AWS, or GCP).
• Strong programming skills in Python and experience with relevant AI/ML frameworks (e.g. TensorFlow, PyTorch, Scikit-learn). 
• Experience working with large structured and unstructured datasets. 
• Understanding of machine learning, predictive analytics, and generative AI concepts.
• Clear communication skills and ability to work effectively in a team environment.

• Strong foundational skills in AWS, event-driven architecture, CQRS, and Kafka.
• Experience designing AI architectures using AWS Bedrock. 
• Previous experience working in renewable energy or highly regulated environments is beneficial. 

Benefits

• Remote-first working with flexibility around client needs.
• Private Medical Insurance. 
• Opportunity to work on meaningful AI projects across multiple industries.
• A collaborative culture focused on learning, excellence and continuous improvement.
• Competitive salary with clear progression pathways.