- Company Name
- Lloyd's
- Job Title
- AI Engineer
- Job Description
-
**Job Title**
AI Engineer
**Role Summary**
Design, implement, and operate AI and ML solutions within a Databricks Lakehouse environment, driving adoption of generative AI tools and ensuring robust governance, security, and performance across enterprise workflows.
**Expectations**
- Deliver end‑to‑end AI pipelines that are secure, scalable, and aligned with corporate Data & AI strategy.
- Demonstrate ownership of model deployment, monitoring, and continuous improvement.
- Mentor teammates on best practices in AI engineering and data visualization.
**Key Responsibilities**
- Architect, build, and maintain Lakehouse pipelines (Databricks SQL, Delta Lake, Unity Catalog) with Azure governance.
- Deploy and monitor ML models with CI/CD, automated testing, drift detection, and quality controls.
- Integrate generative AI (Databricks Genie, LLM orchestration, RAG, vector search) into business workflows for automation and advanced analytics.
- Develop and refine the AI Marketplace, enabling self‑service analytics and new capability adoption.
- Conduct performance telemetry, establish SLOs, and optimize cost and reliability of data pipelines.
- Lead cross‑functional collaboration, code reviews, and technical knowledge sharing.
- Research emerging AI/ML/visualization trends, translate insights into actionable tooling enhancements.
**Required Skills**
- Databricks Lakehouse architecture: SQL, Delta Lake, pipeline orchestration, Unity Catalog.
- Azure data governance, performance tuning, and security best practices.
- ML engineering & MLOps: Python, SQL, CI/CD, automated testing, model monitoring, drift/quality controls.
- Generative AI: LLM orchestration, secure prompting, RAG, vector search, GenAI PoC delivery.
- Solution design: reference architectures, NFR definition, Agile delivery, SDLC ownership.
- Observability: telemetry, metrics, SLOs, cost optimization.
- Collaboration & leadership: cross‑functional partnership, mentoring, standards enforcement.
**Required Education & Certifications**
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field.
- Relevant professional certifications (e.g., Microsoft Azure Data Engineer, Databricks Certified Data Engineer, or ML Ops certifications) preferred.