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Job Description & How to Apply Below
Context
A large, international company in the engineering sector is scaling production-grade AI across the organization. The Data & AI Platform team is expanding delivery capacity and is looking for an interim Data & AI Engineer who can step in immediately, take ownership of AI use cases end-to-end, and accelerate adoption across business teams on an Azure + Databricks foundation.
This is a hands‑on delivery role with high autonomy: you will build, ship, operate, and improve AI solutions in production—while introducing reusable components and pragmatic best practices that raise the bar across the platform.
What you’ll do- Deliver AI use cases end-to-end
: from ingestion and feature engineering to model/agent development and production rollout. - Design and operate Databricks lakehouse pipelines (batch and streaming) using Spark/SQL/Delta Lake, including monitoring and data quality controls.
- Build AI solutions on the platform
, including: - RAG patterns (retrieval, chunking, embeddings, evaluation)
- tool‑using agents and orchestration approaches
- prompt strategies and testing/guardrails
- (where relevant) custom ML models and supporting pipelines
- Productionize and run what you build
: reliability, observability, cost control, and operational hygiene. - Enable other teams by creating reusable components, templates, and delivery standards.
- Work with governance and compliance
: align with AI governance requirements and ensure solutions are secure and auditable. - Collaborate with stakeholders across IT and the business to translate needs into working solutions and clear delivery increments.
- Rapidly understand the current Azure/Databricks landscape and delivery priorities.
- Pick up 1–2 active use cases and move them toward production-quality standards.
- Strengthen delivery patterns (templates, evaluation approach, monitoring, data quality checks).
- Create momentum with visible working increments and pragmatic documentation.
- Proven experience as a Data Engineer / Data & AI Engineer delivering solutions into production environments.
- Strong hands‑on Databricks expertise:
Spark/SQL, Delta Lake, Jobs/Workflows, performance tuning. - Strong Python + SQL for data engineering and AI/ML workflows.
- Experience building data pipelines with quality checks and operational monitoring.
- Practical experience with LLM-based solutions (RAG and/or agents), including prompt strategies and evaluation approaches.
- Comfortable working independently in an interim context: you can own delivery
, communicate clearly, and unblock yourself.
- LLM toolkits (Lang Chain, Semantic Kernel), prompt evaluation frameworks, early LLMOps patterns.
- CI/CD (Git Hub Actions) and Infrastructure-as-Code (Terraform).
- ML frameworks (PyTorch, Tensor Flow, scikit-learn) where needed.
- Immediate impact: deliver AI use cases into production on a modern Azure Databricks platform.
- High ownership and autonomy: a true interim role where delivery outcomes matter.
- Real-world relevance: projects tied to large-scale operations in a complex, safety- and compliance-aware environment.
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