MLOps Engineer

Role : MLOps Engineer

Experience : 3+ years

Location : Gurugram

Work Mode : Hybrid

Required Skills : Python, Bash scripting, YAML/JSON configuration, Linux systems, CI/CD, GitHub Actions, MLflow, Kubeflow, DVC, Prometheus, Grafana.

🔍 What You’ll Do (Key Responsibilities):

• Deploy & Manage: Own the deployment of AI/ML models across development, staging, and production environments.
• Automate Pipelines: Build and maintain seamless CI/CD pipelines for automated ML workflows.
• Monitor & Optimize: Implement real-time monitoring to track model drift, latency, and inference performance.
• Collaborate: Partner closely with our Solution Architect and MLOps Lead to standardize and scale our deployment infrastructure.
• Ensure Reliability: Guarantee model reproducibility, version control, and smooth rollback capabilities.
• Integrate & Audit: Connect AI services with standard APIs and observability frameworks while maintaining comprehensive deployment logs for audit readiness.

🛠️ Technical Toolbox We’re Looking For:

• Core Skills: Python, Bash scripting, YAML/JSON configuration, Linux systems.
• Infrastructure & Orchestration: Docker, Kubernetes.
• CI/CD & IaC: Jenkins, GitLab CI/CD, GitHub Actions, Terraform.
• ML Lifecycle & Data: MLflow, Kubeflow, DVC.
• Monitoring & Logging: Prometheus, Grafana, ELK Stack.
• Cloud Platforms: Experience with AWS SageMaker, Azure ML Studio, or GCP Vertex AI.
• Governance: Knowledge of traceability and Responsible AI compliance in deployment.

🎓 Who You Are (Qualifications & Experience):

• Experience: 3–6 years of hands-on experience operationalizing AI/ML models with a strong focus on CI/CD automation.
• Domain Knowledge: Prior exposure to deploying ML pipelines for NLP, computer vision, or speech systems.
• Education: B.Tech / M.Tech in Computer Science, AI/ML, or a related discipline.
• Bonus Points:
• Certifications in DevOps or Cloud Infrastructure (AWS, Azure, or GCP).
• Published research papers, case studies, or significant open-source contributions.