We are seeking a highly skilled Machine Learning Engineer / MLOps Engineer to design, build, and deploy scalable machine learning systems. This role sits at the intersection of data science, software engineering, and DevOps, with a strong emphasis on productionizing models and maintaining robust ML pipelines.

Experience required - 6 - 8 years

Key Responsibilities

- Design, develop, and deploy machine learning models at scale
- Build and maintain end-to-end ML pipelines using up-to-date MLOps practices
- Containerize applications and workflows using Docker
- Orchestrate ML workflows with Kubeflow or similar platforms
- Collaborate with data scientists to operationalize statistical and machine learning models
- Implement CI/CD pipelines for ML systems and data workflows
- Ensure reliability, scalability, and performance of ML infrastructure
- Apply advanced statistical modeling techniques to solve complex business problems
- Write clean, modular, and maintainable code using object-oriented programming principles
- Monitor, evaluate, and continuously improve deployed models

Require Qualification

- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field
- Strong experience in Machine Learning and Data Science




- Solid understanding of Statistical Modeling and Advanced Statistics
- Hands-on experience with Docker and containerized environments
- Experience with Kubeflow or other ML orchestration tools (e.g., Airflow, MLflow)
- Proficiency in at least one programming language (Python preferred)
- Strong knowledge of Object-Oriented Programming (OOP)
- Experience with CI/CD pipelines and DevOps practices
- Familiarity with cloud platforms (GCP, or Azure)

Preferred Qualifications

- Experience with large-scale distributed systems
- Knowledge of feature stores, model versioning, and monitoring tools
- Experience in deploying real-time or batch ML systems
- Familiarity with infrastructure-as-code (e.g., Terraform)
- Understanding of data engineering concepts and big data tools

Key Skills

- Machine Learning & Deep Learning
- MLOps & Model Lifecycle Management
- Docker & Containerization
- Kubeflow & Workflow Orchestration
- Statistical Analysis & Advanced Modeling
- Object-Oriented Programming
- CI/CD & DevOps Practices

If you are interested in this opportunity, please click on the Apply Now button to submit your application.

📌 ML Engineer (Gujarat)
🏢 INX
📍 Gujarat