Machine Learning Engineer

Job Description: Machine Learning Engineer – MLOps & AI Infrastructure

Company: Indium Software
Job Title: Machine Learning Engineer – MLOps & AI Infrastructure
Experience: 1–3 Years
Job Function: Machine Learning, MLOps, AI Infrastructure, Cloud Engineering

About Indium Software

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.

Job Summary

We are looking for a skilled Machine Learning Engineer specializing in MLOps and AI Infrastructure to develop, deploy, automate, and maintain scalable machine-learning systems. The ideal candidate should have hands-on experience with Python, machine-learning pipelines, model deployment, containerization, CI/CD, and cloud-based machine-learning platforms.

The candidate will collaborate with data scientists, software engineers, and DevOps teams to move machine-learning models from experimentation to reliable production environments.

Key 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.

Required Technical Skills

• 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.

Preferred Skills

• 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.

Eligibility Criteria

• 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 debugging, analytical, and problem-solving skills.
• Ability to explain ML deployment architecture, pipeline automation, and production troubleshooting.
• Good communication and collaboration skills.

Ideal Candidate Profile

Candidates who have deployed machine-learning models into production, automated ML pipelines, implemented model versioning, and worked with Docker, Kubernetes, CI/CD, or cloud ML platforms will be preferred.

Candidates with practical MLOps projects, GitHub repositories, or experience resolving real-world model deployment issues will have an advantage.

Selection 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.

Key Technologies

Python | Machine Learning | MLflow | Kubeflow | Docker | Kubernetes | CI/CD | AWS SageMaker | Azure Machine Learning | SQL | REST APIs | Git | Model Deployment | Model Monitoring | Apache Airflow | Terraform | Prometheus | Grafana

Pay: ₹1,200,000.00 - ₹1,600,000.00 per year

Work Location: In person