Artificial Intelligence Engineer

About the role

As an AI Engineer at Albatha Group, you embed with our customers to turn AI from a promising demo into a production system that moves their business. You will own the full arc: understand the customer's problem, design and build LLM-powered solutions, deploy them into messy real-world environments, and feed what you learn back into our product.

This role suits engineers who like being close to the problem, can write production-grade code, and are equally comfortable in a whiteboard session with a VP and a debugging session with a data team.

What you'll do

• Embed with customer teams to understand their workflows, data, and constraints, and identify where AI can deliver measurable value.
• Scope and prioritize use cases with technical and business stakeholders, turning ambiguous problems into clear delivery plans.
• Design and build production AI solutions: LLM-based agents, retrieval-augmented generation (RAG), tool/function calling, document processing, and workflow automation.
• Integrate with customer systems such as data warehouses, CRMs, ticketing tools, internal APIs, and legacy software.
• Build evaluation harnesses and monitoring so quality, latency, cost, and safety are measured, not guessed.
• Deploy and operate solutions in customer environments, including cloud, VPC, and on-prem setups with security and compliance requirements.
• Iterate with end users after launch, tuning prompts, retrieval, models, and UX based on real usage.
• Turn repeated customer needs into reusable components, and bring product and research teams concrete feedback from the field.
• Present results, trade-offs, and roadmaps to executives and engineers alike.

What you'll bring

• 5+ years of AI experience, with strong Python and working fluency in TypeScript or a similar language.
• Hands-on experience shipping LLM applications to production: prompt design, RAG, agents, tool use, structured outputs, and fine-tuning where it fits.
• Practical knowledge of evaluating AI systems, including building test sets, using LLM-as-judge methods, and tracking regressions.
• Experience with vector search, embeddings, and data pipelines, plus solid SQL and API integration skills.
• Comfort with cloud infrastructure (AWS, GCP, or Azure), containers, and CI/CD.
• Clear communication with both technical and non-technical audiences, including the ability to run workshops and explain trade-offs plainly.
• A bias for ownership: you take a problem from vague request to deployed, working solution.
• Willingness to travel to customer sites as needed (see logistics below).

Nice to have

• Prior experience in a forward deployed, solutions engineering, consulting, or startup founding role.
• Experience with agent frameworks, Model Context Protocol (MCP), or multi-agent orchestration.
• Background in a regulated industry such as finance, healthcare, or government, including security reviews and data governance.
• Familiarity with model serving, inference optimization, or open-weight model deployment.
• Experience with front-end work to build quick internal tools and prototypes for customers.
• Contributions to open-source AI tooling or published technical writing.