Senior ML Engineer

RapidBrains; India; On-site; INR 4,200,000 - 6,600,000; Full time

Job summary

RapidBrains is seeking a Senior Machine Learning Engineer to design, deploy, and operationalize enterprise ML and GenAI systems on Google Cloud Platform and Snowflake Cortex AI.

In this production-focused role, you will build scalable ML solutions, semantic search architectures, and robust MLOps pipelines, collaborating with data engineers and product teams.

Candidates should have deep cloud experience, hands-on model deployments, and a strong bias for reliability.

Qualifications

• Hands-on experience with GCP Vertex AI, Vertex AI Pipelines, and BigQuery Vector Search.
• Deep understanding of product similarity models, transformer architectures, vector embeddings, and kNN algorithms.
• Proven experience leveraging Snowflake Cortex AI and prompt engineering.

Responsibilities

• Design and deploy production ML solutions on GCP using Model Garden and Batch Inference.
• Build scalable semantic and similarity search systems with embeddings and kNN models.
• Architect and maintain end-to-end MLOps pipelines using Vertex AI Pipelines, Cloud Build, and Cloud Run.
• Develop prompt engineering solutions and in-database AI operations using Snowflake Cortex AI.
• Automate build, test, and deployment workflows with CI/CD tools (GitHub Actions, Cloud Build).

Skills

Vertex AI

BigQuery Vector Search

Model Garden

Transformer Models

kNN Algorithms

Snowflake Cortex AI

Prompt Engineering

Docker

GitHub Actions

Vertex AI Pipelines

Cloud Run

Terraform

Tools

Docker

GitHub Actions

Vertex AI Pipelines

BigQuery

Snowflake Cortex AI

Terraform

Job description

Job Type: Full-time/Permanent employment

We are looking for a Senior Machine Learning Engineer to design, deploy, and operationalize enterprise ML and GenAI systems. You will work on production-grade machine learning solutions, semantic search architectures, and automated MLOps pipelines on Google Cloud Platform (GCP) and Snowflake's Cortex AI.

What You Will Do

• Design and deploy production ML solutions on GCP using Model Garden, LLM Evaluation frameworks, and Batch Inference pipelines.
• Build scalable semantic and similarity search systems using BigQuery Vector Search, Transformer embeddings, and kNN-based models.
• Architect and maintain end-to-end MLOps pipelines using Vertex AI Pipelines, Cloud Build, and Cloud Run jobs.
• Develop prompt engineering solutions and execute in-database AI operations using Snowflake Cortex AI.
• Automate build, test, and deployment workflows using CI/CD tools (GitHub Actions, Cloud Build).

What We Are Looking For

• Core Cloud ML: Hands-on experience with GCP (Vertex AI, Vertex AI Pipelines, Model Garden, BigQuery Vector Search, Cloud Run, Cloud Build).
• GenAI & AI Modeling: Deep understanding of product similarity models, Transformer architectures, vector embeddings, and kNN algorithms.
• Snowflake AI: Proven experience leveraging Snowflake Cortex AI and prompt engineering.
• Software Engineering: Strong foundation in containerization (Docker) and Cloud IAM.

Nice-to-Have

• Experience with Airflow and dbt for data orchestration.
• Familiarity with Infrastructure as Code (Terraform) and data modeling.
• Continuous integration experience with GitHub Actions