AI Engineer

Description

· Participate in developing Generative AI & Traditional AI Platform Capabilities on enterprise on-prem and cloud platforms.

· Responsible for AI model delivery to on-prem infrastructure and cloud platforms (GCP-Vertex AI, Azure ML)

· Collaborating with Data scientist to optimize the scoring pipeline.

· Building automation capabilities to deploy ML Models and LLM Models on the enterprise on-prem platform and cloud platform.

· Build and Deploy capabilities for automating model scoring/Inferencing of ML models and LLMs.

· Build and Deploy capabilities for data pipeline deployment standardization and model consumption by multiple LOBs.

· Collaborate with product owners, devOps team, data scientists, support teams to define and drive end to end model scoring pipelines.

· Participate in day-to-day standups for platform capability build.

· Provide SME guidance for data science teams on software engineering principles, model deployments, platform capabilities.

· Drive AI use case delivery end to end collaborating with Data scientists, Data Engineers, LOB Technology using standardized platform processes and capabilities.

· Support Production Issues partnering with production support.

Responsibilities

• · 5+ years of Python experience
• · 5+ years of big data experience needed (Big Query, Hadoop)
• · 3 years of experience in AIML area (MLOps)
• · 2+ years of experience in developing APIs using Python/FastAPI.
• · 1+ year of Document AI, Agent Builder/GCP search/conversation / Dialogflow – Nice to have
• · Good to have 1+year of experience in LLM, Generative AI (developing capabilities or dev/ops)
• · Good to have Experience in developing of API on GCP/Azure/API Gateways
• · Good to have 1+year of experience in Vector Database, Model Development would be added benefit.