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Software Engineer with applied AI and production experience architecting integrations, APIs, and ML/AI workflows that turn business needs into shipped systems. Strong focus on model training/serving pipelines, data infrastructure, and reliable cloud deployments. Comfortable owning projects from ambiguous requirements to production outcomes, with clear communication to non-technical stakeholders. Experienced building and monitoring ML/AI backend services on Google Cloud Platform that connect AI capabilities to real business impact.…Software Engineer with applied AI and production experience architecting integrations, APIs, and ML/AI workflows that turn business needs into shipped systems. Strong focus on model training/serving pipelines, data infrastructure, and reliable cloud deployments. Comfortable owning projects from ambiguous requirements to production outcomes, with clear communication to non-technical stakeholders. Experienced building and monitoring ML/AI backend services on Google Cloud Platform that connect AI capabilities to real business impact.

Sainikhil Juluri

Software Engineer, AI Engineer, Data Engineer, +1

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Software Engineer with applied AI and production experience architecting integrations, APIs, and ML/AI workflows that turn business needs into shipped systems. Strong focus on model training/serving pipelines, data infrastructure, and reliable cloud deployments. Comfortable owning projects from ambiguous requirements to production outcomes, with clear communication to non-technical stakeholders. Experienced building and monitoring ML/AI backend services on Google Cloud Platform that connect AI capabilities to real business impact.…Software Engineer with applied AI and production experience architecting integrations, APIs, and ML/AI workflows that turn business needs into shipped systems. Strong focus on model training/serving pipelines, data infrastructure, and reliable cloud deployments. Comfortable owning projects from ambiguous requirements to production outcomes, with clear communication to non-technical stakeholders. Experienced building and monitoring ML/AI backend services on Google Cloud Platform that connect AI capabilities to real business impact.

Available to hire

Software Engineer with applied AI and production experience architecting integrations, APIs, and ML/AI workflows that turn business needs into shipped systems. Strong focus on model training/serving pipelines, data infrastructure, and reliable cloud deployments.

Comfortable owning projects from ambiguous requirements to production outcomes, with clear communication to non-technical stakeholders. Experienced building and monitoring ML/AI backend services on Google Cloud Platform that connect AI capabilities to real business impact.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Work Experience

AI Engineer at Delight Foods LLC

March 1, 2026 - Present

Architect and build backend APIs and integrations connecting model training, serving, and analytics workflows across teams. Own design and deployment of ML pipelines and supporting infrastructure on Google Cloud Platform, including embeddings pipelines and retrieval (RAG) systems using vector databases (pgvector). Design scalable data pipelines using Python and SQL to assemble large, complex, production-ready datasets from multiple internal and external sources. Deploy and monitor production systems on GCP, troubleshooting performance and reliability issues to keep downstream consumers running smoothly.

Graduate Assistant (Data Engineering & Analytics) at San Jose State University

January 1, 2025 - April 1, 2025

Built and optimized data pipelines for structured research datasets and supported analytics workflows using regression, classification, and clustering models. Delivered data-driven insights and reports to non-technical stakeholders.

Junior Data Scientist (Data Engineer Focus) at Netorbit Inc

August 1, 2022 - December 1, 2023

Designed and implemented data pipelines to assemble structured and semi-structured enterprise data from multiple sources into datasets usable for modeling and analytics. Contributed to model experimentation and feature engineering for predictive analytics by translating business requirements into decision-making tools. Applied Python (OOP) and SQL-based processing to clean, transform, and optimize large, complex datasets. Improved data quality through validation checks and standardized transformations, documenting processes for maintainability.