HyrEzy Talent Solutions

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Experience Range: 4–8 Years | Location: Bangalore / Remote
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Position Overview
As an MLOps and AI Infrastructure Engineer, you sit at the intersection of software engineering, data science, and cloud operations. You will be responsible for building, scaling, and optimizing the underlying pipelines, model-serving architectures, and infrastructure required to run large-scale machine learning and generative AI workloads efficiently in production.
Key Responsibilities &amp; Scope
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Model Deployment &amp; Serving: Architect and maintain scalable model deployment pipelines for LLMs, deep learning models, and predictive analytics engines using tools like Triton, Ray, or BentoML.
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Pipeline Automation: Build automated CI/CD pipelines for machine learning (ML pipelines) covering data ingestion, feature stores, model training, validation, and monitoring.
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Infrastructure Cost &amp; GPU Optimization: Monitor GPU/CPU cluster utilization, manage container orchestration (Kubernetes), and optimize cloud compute expenditures for heavy AI workloads.
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Collaboration: Work closely with Data Scientists and AI Researchers to transition experimental models into resilient, low-latency production services.
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Must-Have Qualifications
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Experience: 4–8 years of experience in cloud infrastructure, DevOps, or backend engineering with a heavy focus on ML systems.
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Tech Stack: Deep proficiency in Python, Docker, Kubernetes, Terraform, and cloud platforms (AWS Bedrock, SageMaker, GCP Vertex AI, or similar).
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Domain Expertise: Hands-on familiarity with vector databases (Pinecone, Milvus, Qdrant) and LLM orchestration frameworks.
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