Harman is hiring Software Engineer – ML Ops DevOps Job Referral By FLM

• Role: Software Engineer – ML Ops DevOps
• Experience: 3–6 Years

Key Responsibilities

• Design and implement end-to-end MLOps pipelines for model development, training, deployment, and monitoring.

• Build and manage CI/CD pipelines using GitLab CI, Jenkins, and Argo CD.

• Deploy and manage machine learning workloads on AWS and Azure.

• Develop and maintain containerized applications using Docker and Kubernetes.

• Automate infrastructure provisioning using Terraform (Infrastructure as Code).

• Deploy and manage ML models using Kubernetes and microservices architecture.

• Monitor ML models for performance, reliability, and model drift.

• Build automated pipelines for data ingestion, model training, validation, versioning, and deployment.

• Collaborate with Data Scientists, Software Engineers, and DevOps teams to integrate ML models into production.

• Follow DevOps, security, and cloud governance best practices.

Required Skills

• AI/ML Engineering

• DevOps / MLOps

• CI/CD (GitLab CI, Jenkins, Argo CD)

• AWS or Azure

• Docker

• Kubernetes

• Terraform (Infrastructure as Code)

• Python

• Machine Learning Lifecycle

• Model Deployment & Monitoring

• Data Pipeline Automation

• Microservices Architecture

• Distributed Systems

• Agile Methodology

• Strong Analytical & Problem-Solving Skills

Preferred Skills

• TensorFlow

• PyTorch

• Scikit-learn

• MLflow

• Kubeflow

• AWS SageMaker

• Apache Kafka

• Prometheus

• Grafana

• Logging & Monitoring Tools

• Experience in Automotive or Embedded AI environments

Eligibility

• Bachelor’s or Master’s degree in Computer Science, Data Science, Electronics, IT, or a related field.

• 3–6 years of experience in AI/ML Engineering and DevOps/MLOps.

• Hands-on experience with cloud-native ML deployments and CI/CD pipeline automation.

• Strong expertise in integrating AI/ML workflows with DevOps practices.