Machine Learning Engineer

About This Role

This Machine Learning Engineer role focuses on MLOps and AI Infrastructure, developing, deploying, automating, and maintaining scalable machine-learning systems. The position suits a candidate with hands-on experience in Python, machine-learning pipelines, model deployment, containerization, CI/CD, and cloud-based machine-learning platforms. You will collaborate with data scientists, software engineers, and DevOps teams to move machine-learning models from experimentation to reliable production environments.

Responsibilities

• Design, develop, and maintain end-to-end machine-learning pipelines
• Automate model training, testing, validation, deployment, and retraining workflows
• Implement experiment tracking, model versioning, and model registry using MLflow or similar tools
• Build and manage ML workflows using Kubeflow, Apache Airflow, or equivalent orchestration tools
• Containerize machine-learning applications using Docker and deploy them using Kubernetes
• Develop CI/CD pipelines for automated testing and deployment of ML models
• Deploy and manage ML solutions using AWS SageMaker and Azure Machine Learning
• Monitor model performance, data drift, prediction accuracy, latency, and system reliability
• Develop REST APIs to integrate machine-learning models with business applications
• Optimize model-serving infrastructure for scalability, reliability, and cost efficiency
• Collaborate with data engineering teams to prepare data pipelines and ensure data quality
• Implement logging, monitoring, testing, and reproducibility practices across ML workflows
• Troubleshoot production issues and improve deployed AI/ML system performance

Requirements

• 1–3 years of relevant experience in machine-learning engineering, MLOps, AI infrastructure, or ML deployment
• Practical experience with at least one end-to-end ML deployment project
• Strong proficiency in Python programming
• Understanding of machine-learning algorithms and model development workflows
• Hands-on experience with MLflow or similar experiment-tracking and model-management tools
• Knowledge of Docker and containerization
• Familiarity with Kubernetes and container orchestration
• Experience with CI/CD pipelines and automated deployment workflows
• Knowledge of model deployment, model serving, and production monitoring
• Understanding of REST APIs, SQL, Git, and software engineering fundamentals
• Familiarity with cloud platforms such as AWS or Microsoft Azure
• Strong debugging, analytical, and problem-solving skills
• Ability to explain ML deployment architecture, pipeline automation, and production troubleshooting
• Good communication and collaboration skills

Nice to have:

• Experience with Kubeflow, Apache Airflow, or other workflow orchestration tools
• Hands-on exposure to AWS SageMaker or Azure Machine Learning
• Knowledge of model drift detection and model performance monitoring
• Familiarity with feature stores and automated ML pipelines
• Experience with infrastructure-as-code tools such as Terraform
• Understanding of distributed training, scalable inference, and GPU-based workloads
• Exposure to LLM deployment, inference optimization, and production AI infrastructure
• Knowledge of monitoring tools such as Prometheus and Grafana

Application Process

• Resume screening and project evaluation
• Python programming and technical assessment
• Machine-learning and MLOps technical interview
• Practical assessment covering deployment pipelines, Docker, CI/CD, or model monitoring
• Final technical and HR discussion

About the Company

Indium Software is a digital engineering company providing software development, data engineering, artificial intelligence, machine learning, and digital transformation solutions to businesses across multiple industries.