MLOps Engineer

🔥 12 hours ago

🇮🇳 India – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 25%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Shuru

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2021

🤖 Artificial Intelligence

🤝 B2B

🏢 Enterprise

Artificial Intelligence • B2B • Enterprise

Shuru is a product, AI, and technology consulting firm that partners with businesses to deliver strategic consulting, full-cycle product and custom software development, and curated engineering team extension. Their AI-native engineering teams build scalable AI applications, data engineering and analytics, cloud/DevOps, and API integrations to modernize systems and accelerate product delivery. Shuru operates globally with a remote-first model and emphasizes high ownership, design thinking, and measurable outcomes for enterprise and startup clients.

📋 Description

• Build and automate ML pipelines for training, deployment, monitoring, and retraining. • Deploy and manage machine learning solutions on cloud platforms, preferably Azure. • Implement model monitoring, governance, versioning, and performance tracking. • Collaborate with Data Science and Engineering teams to productionize ML models. • Manage cloud-based ML services and infrastructure. • Improve MLOps platforms, tools, and deployment practices. • Work with stakeholders and technology partners to deliver scalable ML solutions.

🎯 Requirements

• 5-8 years of experience in ML Engineering, MLOps, or related roles. • Strong hands-on experience with Python. • Experience with TensorFlow, PyTorch, or Scikit-learn. • Strong understanding of the ML lifecycle and model deployment. • Experience with cloud platforms such as Azure, AWS, or GCP. • Experience building automated CI/CD and ML pipelines. • Knowledge of Docker, Kubernetes, MLflow, or Kubeflow. • Strong communication and stakeholder management skills.

🏖️ Benefits

• Competitive compensation and benefits. • Remote and flexible work environment. • Opportunity to shape technology strategy and business outcomes. • Strong learning and leadership growth opportunities.