Machine Learning Engineer (GCP)

Cognine Technologies Private Limited; Hyderabad; On-site; INR 1,200,000 - 2,000,000; Full time

Job summary

Cognine Technologies Private Limited is seeking an experienced Machine Learning Engineer / MLOps Engineer to design and manage scalable machine learning solutions on Google Cloud Platform (GCP). The role involves working closely with cross-functional teams to ensure successful deployment of ML models.

The ideal candidate has 5-10 years of experience in production ML systems, is proficient in Python, and has hands-on experience with GCP services like Vertex AI. The position offers opportunities for continuous improvement and collaboration across teams.

Qualifications

• 5–10 years of experience in machine learning engineering or MLOps.
• Strong proficiency in Python and scalable data workflows.
• Hands-on experience with GCP services including Vertex AI and BigQuery.

Responsibilities

• Design and maintain ML training and inference pipelines.
• Implement CI/CD for machine learning workflows.
• Deploy models and manage their lifecycle.

Skills

Machine Learning Engineering

MLOps

Python

Google Cloud Platform (GCP)

Tools

Vertex AI

BigQuery

Cloud Storage

Cloud Run

GitHub Actions

Job description

We are looking for an experienced Machine Learning Engineer / MLOps Engineer to design, deploy, and maintain scalable machine learning solutions on Google Cloud Platform (GCP). The ideal candidate will have strong expertise in production ML systems, CI/CD automation, Vertex AI, and model lifecycle management while collaborating closely with cross-functional teams to operationalize machine learning models.

Key Responsibilities

• Design, build, and maintain training and inference pipelines for storm outage prediction workflows.
• Implement CI/CD, orchestration, and automation for machine learning workflows using Vertex AI and related GCP services.
• Deploy machine learning models into production environments and manage model lifecycle processes, including versioning and rollout support.
• Set up and maintain baseline monitoring for model drift, performance, reliability, and alerting.
• Create scalable, production-ready ML workflows and supporting technical documentation.
• Collaborate with data scientists, engineers, and project stakeholders to operationalize models and align deployment architecture with project needs.
• Support troubleshooting, performance tuning, and continuous improvement of ML platform components.
• Contribute to engineering best practices across code quality, release processes, and environment stability.

Required Qualifications

• 5–10 years of experience in machine learning engineering, MLOps, or related production ML engineering roles.
• Strong proficiency in Python and experience developing scalable data and ML workflows.
• Hands‑on experience with Google Cloud Platform, including Vertex AI, BigQuery, Cloud Storage, Cloud Run, Cloud Functions, or comparable services.
• Experience with GitHub Actions or similar CI/CD tooling.
• Demonstrated experience building CI/CD pipelines and automating ML model deployment and orchestration.
• Experience with model monitoring, observability, and production support for machine learning systems.
• Strong understanding of software engineering best practices, including version control, testing, and documentation.
• Ability to work effectively across distributed teams and communicate clearly with technical and non‑technical stakeholders.

Preferred Qualifications

• Experience supporting forecasting, outage prediction, or other data‑intensive operational use cases.
• Familiarity with utility, energy, weather, or geospatial data domains.
• Exposure to model registry, retraining automation, and ML lifecycle governance practices.
• Prior experience working in offshore or globally distributed delivery models.