Senior Machine Learning Engineer

Sr. Machine Learning Engineer

Location: Bangalore Local Candidates Only (Onsite)

Budget: Negotiable

Employment Type: Full-Time

Interview Mode: Virtual

Experience Required: 8+ Years Only

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

• Bachelors or Masters 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 tools such as 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