Participate in the deployment of Generative AI Platform Capabilities
Responsible for AI model delivery to cloud platforms (Azure ML)
Participate in day-to-day standups for platform capability build
Provide SME guidance for data science teams
Research industry best practices, evaluate new technologies, develop standards and engineering best practices, and recommend innovative solutions that support automation and improve platform resiliency and fault tolerance of critical applications
Execute roadmaps that align with technology and business strategy
Assist with hardware and capacity planning, analysis, and forecasts for your portfolio of applications with a focus on the highest availability, scalability, performance, and timely delivery
Act as an expert resource for other technical teams within Data & Analytics
Requirements & Skills:
Bachelor’s Degree in Computer Science or Mathematics/Statistics and 6 or more years of experience in the Data Engineering area OR
High School Diploma or Equivalent and 8 or more years of experience in the Data Engineering area OR
Zurich Certified Insurance Apprentice including an Associate Degree in Computer Science or Mathematics/Statistics and 6 or more years of experience in the Data Engineering area AND
5 or more of experience in Enterprise Data Engineering
Experience in scripting languages
Experience with data transformation and aggregation tools
Experience in data analysis, data interrogation, or data profiling
5+ years of Python experience
5+ years of big data experience needed
3+ years of Pyspark experience
2+ years of experience in developing APIs using Python/FastAPI
3 years of experience in the AIML area (MLOps)
1+ year of experience in LLM, Generative AI (developing capabilities or dev/ops)
1+ years of experience in Vector Database and Model Development
Experience in developing API on GCP/Azure/API Gateways
Working knowledge of Unity Catalog and MLFlow
Strong verbal, written, and interpersonal communication skills
Ability to articulate technical solutions to both technical and business audiences
Working knowledge of design and build grid computing with CPU and GPU supporting AIML and NLP
Working knowledge of high-performance storage technologies along with Object Storage
Knowledge and understanding of network infrastructure to support high throughput and low latency grid computing
