D
Applied AI Engineer
Diagonal recruitment
1 hour ago
Full-time
On-site
As organisations move from AI pilots into practical deployment, demand is growing for engineers capable of building real-world applications powered by large language models and modern AI tooling.
Check all associated application documentation thoroughly before clicking on the apply button at the bottom of this description.
Role Overview
Build AI-powered applications, copilots and agents Integrate foundation models into business workflows Develop prompting, tool-calling and reasoning strategies Evaluate and improve AI application performance Collaborate with product, data and architecture teams Support deployment into production environments Contribute to the design of scalable, maintainable AI systems
Tools & Technologies (required)
Python OpenAI, Anthropic, Gemini or similar LLM platforms LangChain, LangGraph or equivalent frameworks APIs and integration tooling Git Docker AWS, Azure or GCP
Experience (required)
Building and deploying AI applications into production Working across LLMs Implementing RAG architectures Developing agentic AI workflows Integrating AI solutions with business systems and APIs Understanding of evaluation, testing and performance optimisation
About You
Strong software engineering fundamentals Builder mentality with a focus on delivery Comfortable xsgimln moving from prototype to production Focused on solving business problems using AI Practical understanding of model limitations and trade-offs Curious about emerging AI capabilities but grounded in real-world implementation
Additional Information
Permanent, interim and fractional opportunities available Exposure to a broad range of AI use cases and technologies Benefits & Perks among the best around.
Check all associated application documentation thoroughly before clicking on the apply button at the bottom of this description.
Role Overview
Build AI-powered applications, copilots and agents Integrate foundation models into business workflows Develop prompting, tool-calling and reasoning strategies Evaluate and improve AI application performance Collaborate with product, data and architecture teams Support deployment into production environments Contribute to the design of scalable, maintainable AI systems
Tools & Technologies (required)
Python OpenAI, Anthropic, Gemini or similar LLM platforms LangChain, LangGraph or equivalent frameworks APIs and integration tooling Git Docker AWS, Azure or GCP
Experience (required)
Building and deploying AI applications into production Working across LLMs Implementing RAG architectures Developing agentic AI workflows Integrating AI solutions with business systems and APIs Understanding of evaluation, testing and performance optimisation
About You
Strong software engineering fundamentals Builder mentality with a focus on delivery Comfortable xsgimln moving from prototype to production Focused on solving business problems using AI Practical understanding of model limitations and trade-offs Curious about emerging AI capabilities but grounded in real-world implementation
Additional Information
Permanent, interim and fractional opportunities available Exposure to a broad range of AI use cases and technologies Benefits & Perks among the best around.