AI ML Engineer

Key Responsibilities

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.

Key Requirements

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.