Machine Learning Engineer – GCP Vertex AI

Location: Hyderabad, Telangana

Work Mode: Onsite – 5 Days/Week

Employment Type: Full-Time

Compensation: ₹16.8–₹18.0 LPA

Experience: 4–8 Years (flexible based on relevant expertise)

Job Overview

We are looking for a highly skilled Machine Learning Engineer to join our engineering team in Hyderabad. The ideal candidate will have strong hands-on experience in Machine Learning, Python, GCP Vertex AI, ML deployment, cloud infrastructure, and DevOps practices.

You will be responsible for building, deploying, scaling, and maintaining production-grade ML solutions while working closely with data scientists, software engineers, and platform/DevOps teams.

Key Responsibilities

Design, develop, deploy, and maintain production-grade Machine Learning solutions.

Build and manage ML workflows using GCP Vertex AI.

Develop scalable and production-ready applications/services using Python and FastAPI.

Implement distributed ML workloads using Ray where required.

Build and maintain automated ML/data pipelines using Apache Airflow.

Work with SQL and relational databases for data extraction, transformation, and analysis.

Automate cloud infrastructure provisioning and management using Terraform.

Implement CI/CD, monitoring, deployment, and other DevOps practices for ML applications.

Collaborate with Data Scientists, ML Engineers, Software Engineers, and DevOps teams to move models from experimentation to production.

Optimize ML systems for scalability, reliability, performance, and cost.

Troubleshoot production ML pipelines, services, and infrastructure.

Follow best practices for version control, testing, documentation, security, and deployment.

Must-Have Skills

Strong hands-on experience in Machine Learning (ML).

Strong proficiency in Python.

Hands-on experience with GCP Vertex AI.

Experience with Terraform and Infrastructure as Code (IaC).

Strong SQL skills.

Experience developing APIs/services using FastAPI.

Experience with Ray or distributed computing frameworks.

Strong understanding of DevOps practices and CI/CD.

Hands-on experience with Apache Airflow and workflow orchestration.

Experience deploying and operating ML solutions in production environments.

Good understanding of cloud-based ML architecture and scalable systems.

Good to Have

Experience building Agentic AI workflows.

Exposure to Generative AI / LLM-based applications.

Experience with LLM orchestration, tool calling, agents, or multi-agent workflows.

Experience with ML model serving and monitoring.

Familiarity with Docker/Kubernetes.

Experience with MLOps practices and platforms.

Ideal Candidate

The ideal candidate is a hands-on ML Engineer who can work across the complete ML lifecycle — from model development and data pipelines to cloud deployment, infrastructure automation, APIs, monitoring, and production support.

Candidates with strong experience in GCP Vertex AI + Python + Terraform + FastAPI + Ray + Airflow + DevOps will be preferred.