AI Engineer / AI Use Case Expert

The role

You will lead the hands-on design, development, testing and rollout of AI, machine learning and GenAI use cases. You will turn business opportunities into practical, measurable AI solutions that are ready for production, working closely with business, architecture, data, security and application teams to take each use case from idea to proof of concept, pilot and production.

What you will do

AI use case design and prioritization

• Find and assess AI opportunities by business value, feasibility, data availability, complexity and risk.

• Turn business problems into clearly defined AI, ML and GenAI use cases with technical requirements.

• Set measurable success criteria and help prioritize quick wins and strategic use cases.

Hands-on AI, ML and GenAI engineering

• Design, build and test solutions for anomaly detection, classification, risk scoring, prediction, pattern detection and intelligent automation.

• Build enterprise GenAI capabilities with LLMs, Azure OpenAI, RAG, embeddings, semantic and vector search, and document intelligence.

• Design controlled AI assistants and AI agents that work with enterprise APIs, data and workflows.

• Use Python, SQL and the relevant AI and ML frameworks for development, prototyping and troubleshooting.

Data and AI integration

• Work with data teams to profile, cleanse, transform and prepare AI-ready datasets.

• Check that data is complete, of good quality and suitable for the model.

• Integrate AI models and services with enterprise applications, APIs and data platforms.

• Support structured, semi-structured and unstructured data use cases.

Responsible AI, security and quality

• Put Responsible AI controls in place for accuracy, explainability, privacy, security, bias and human oversight.

• Add human-in-the-loop controls for sensitive or high-impact AI decisions.

• Test GenAI solutions for correctness, groundedness, consistency, hallucination, prompt injection and data leakage.

• Define evaluation criteria and quality thresholds that must be met before production.

MLOps, LLMOps and production

• Set up versioning, testing and deployment for models, prompts and configuration.

• Monitor model performance, drift, failures and usage.

• Support secure production deployment and keep improving AI capabilities over time.

• Keep models, prompts, data and outputs traceable and auditable.

What you bring

• 7+ years in data, analytics or software engineering, with strong recent delivery of AI, ML and GenAI work.

• Strong hands-on Python, SQL, Azure AI and Azure OpenAI.

• Practical experience with LLMs, RAG, AI agents, machine learning, vector search, document intelligence and enterprise integration.

• Experience taking AI solutions from use case definition through proof of concept and pilot to production.

• Knowledge of MLOps and LLMOps, AI security and Responsible AI.

Certifications

• Microsoft Certified: Azure AI Engineer Associate, preferred.

• Data engineering or data science certifications are a plus.