- Workplace
- Hybrid
- Type
- Full-time
- Department
- Retail
- Closing date
- Today
- Source
- Vincere
Description
Key Responsibilities
- Build and maintain production-grade data pipelines for training, fine-tuning, embeddings, and real-time inference using Python and Databricks.
- Implement and improve RAG and agent-based retrieval (vector DBs, re-ranking, routing, hybrid search).
- Support MLOps/LLMOps (experiment tracking, model registry, evaluations, drift detection, retraining, cost/token monitoring).
- Monitor AI system health (hallucination, retrieval quality, prompt drift, token usage) and help automate remediation.
- Collaborate with product and engineering teams to ship features quickly and reliably.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related discipline; advanced degree is an advantage.
- 1+ years of hands-on data engineering or ML engineering experience (internships and full-time roles considered).
- Strong data engineering foundation, with proficiency in Python and production-grade SQL.
- Familiarity with at least one major cloud platform (AWS, Google Cloud, or Azure).
- Practical experience with LLMs / generative AI in production or advanced prototypes (e.g. LangChain or similar frameworks) is highly desirable.
- Solid software engineering skills, including use of Git-based workflows, CI/CD pipelines, and API service design.
- Comfortable working with AI-assisted coding tools and incorporating them into production development workflows.
Skills
PythonAWSAzureCI/CDSQLDatabricksData EngineeringGit
