Machine Learning Engineer / MLOps Engineer

Swediumglobal; Bengaluru; Hybrid; INR 1,200,000 - 1,800,000; Full time

Job summary

Swediumglobal is seeking a seasoned Machine Learning Engineer / MLOps Engineer in Bengaluru. You will design, build, and deploy machine learning systems, collaborating with data scientists and developing robust ML pipelines. This role requires extensive experience in Machine Learning, strong programming skills (Python preferred), and familiarity with tools like Docker and Kubeflow. The position offers a hybrid work model, ideal for professionals with 7+ years in Data Science and strong statistical modeling skills.

Qualifications

• 7+ years of strong experience in Machine Learning and Data Science.
• Hands-on experience with Docker and containerized environments.
• Strong knowledge of Object-Oriented Programming (OOP).

Responsibilities

• Design, develop, and deploy machine learning models at scale.
• Build and maintain end-to-end ML pipelines using modern MLOps practices.
• Collaborate with data scientists to operationalize statistical and machine learning models.

Skills

MLOps & Model Lifecycle Management

Docker & Containerization

Kubeflow & Workflow Orchestration

Statistical Analysis & Advanced Modeling

Object-Oriented Programming

CI/CD & DevOps Practices

Education

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field

Tools

Docker

Kubeflow

CI/CD pipelines

Job description

Swedium Globalis the growingSystemEngineering and Solution Company, offers services like Semiconductor Engineering R&D Services, EmbeddedSystems Development, CustomApplicationSoftwareDevelopment, Web and CloudApplicationDevelopment, Testing Services, Consultancy and Outsourcing services to our clients across the globe for an onsite and offshore business model. Swedium Global is having presence in Sweden, Finland, Poland, Czech Republic and in India.

Experience: 7+Years
Location:Bangalore (Hybrid)
Notice Period:Immediate to 15 Days

About the Role

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.

Key Responsibilities

• Design, develop, and deploy machine learning models at scale
• Build and maintain end-to-end ML pipelines using modern 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

Required Qualifications

• 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

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

Job Overview

• Vacancy : 1
• Key Skills : Machine Learning, Deep Learning, MLOps, Docker, Kubeflow, Python, Statistical Modeling, Advanced Statistics, CI/CD, DevOps, Object-Oriented Programming (OOP), ML Pipelines, Model Deployment, Model Monitoring, GCP, Azure, Terraform, Data Engineering, Spark