MLOps Engineer

Overview:

CACTUS is a remote-first organization and we embrace an accelerate from anywhere culture. You may be

required to travel to our Mumbai office based on business requirements or for company/team events.

We are looking for an MLOps Engineer to operationalize and manage the deployment of AI/ML models

across development, staging, and production environments. In this role, you will build automated CI/CD

pipelines, implement real-time monitoring for model drift and performance, and ensure the

reproducibility and version control of all deployed systems. Collaborating closely with architects and leads,

you will standardize infrastructure while maintaining strict audit readiness and compliance with

Responsible AI standards. If you have a strong background in cloud infrastructure and a passion for

bridging the gap between model development and scalable production, this role offers a key position in

our technical ecosystem.

Responsibilities:

• Deploy and manage AI/ML models in development, staging, and production environments.

• Build and maintain automated pipelines for continuous integration and delivery (CI/CD).

• Implement real-time monitoring for model drift, latency, and inference performance.

• Collaborate with Solution Architect and MLOps Lead to standardize deployment infrastructure.

• Ensure reproducibility, rollback, and version control for deployed models.

• Integrate AI services with APIs and observability frameworks.

• Maintain deployment logs, error reports, and environment snapshots for audit readiness

Requirements:

• B.Tech / M.Tech in Computer Science, AI/ML, or related discipline.

• Certifications in DevOps or Cloud Infrastructure (AWS, Azure, or GCP) preferred.

• Research papers, case studies, or significant open source contributions(are preferred)

• 3–6 years in operationalizing AI/ML models with proven experience in CI/CD automation.

• Prior exposure to deploying ML pipelines for NLP, computer vision, or speech systems.

• Familiarity with monitoring, model lifecycle management, and performance logging.

• Candidates with a background in government projects will be at a distinct advantage.

Technical Competencies:

• Infrastructure Tools: Jenkins, GitLab CI/CD, Docker, Kubernetes.

• Monitoring & Logging: Prometheus, Grafana, ELK Stack.

• ML Lifecycle Management: MLflow, Kubeflow, DVC.

• Cloud Platforms: AWS SageMaker, Azure ML Studio, GCP Vertex AI.

• Core: Python, Bash scripting, YAML/JSON configuration, Linux systems.

• CI/CD: Jenkins, GitLab CI, or GitHub Actions for automated deployments, Terraform

• Governance: Traceability and Responsible AI compliance in deployment.

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