Hiring: MLOps Engineer (4–6 Years Experience)

Role Overview

We are looking for a highly driven MLOps Engineer with 4–6 years of experience to help scale and productionize ML systems across real-world enterprise use cases. This role requires strong backend and cloud fundamentals along with hands-on ML lifecycle ownership.

Key Responsibilities

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Design and manage end-to-end ML pipelines (training to deployment to monitoring) Build scalable model serving infrastructure (real-time and batch) Implement CI/CD pipelines for ML workflows Automate model retraining and versioning

Ensure monitoring, logging, and alerting for ML systems Optimize infrastructure cost and performance Maintain data pipelines and feature stores Manage containerized workloads and orchestration

Required Skills & Experience

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4–6 years of experience in backend / MLOps / ML infrastructure roles Strong Python skills Hands-on experience with AWS (ECS / EKS / Lambda / S3 / IAM / CloudWatch) Experience with Docker and container orchestration CI/CD tools (GitHub Actions / GitLab / Jenkins) Experience with ML lifecycle tools (MLflow / SageMaker / Kubeflow / Airflow / Prefect) Experience deploying models as APIs Strong understanding of monitoring, versioning, and Infrastructure as Code (Terraform preferred)

Good to Have

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Experience with LLM systems / RAG pipelines Experience working in microservices architecture Knowledge of DORA metrics & DevOps best practices Exposure to SOC2-compliant production environments

What We Expect

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Strong ownership mindset Ability to debug production issues independently Clear understanding of scalability, reliability, and failure modes

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Comfort working in fast-moving, startup-style environments