We are looking for a skilled MLOps Engineer with 3–5 years of experience to build, automate, and manage machine learning infrastructure and deployment pipelines. The ideal candidate will work closely with Data Scientists, Data Engineers, and Cloud teams to streamline the end-to-end ML lifecycle, from project setup and model packaging to deployment, monitoring, and governance.

You will be responsible for developing scalable MLOps solutions that enable faster model development, reliable production deployments, and automated operational processes across cloud platforms.

Key Responsibilities

- Design, develop, and maintain MLOps platforms and automation frameworks for machine learning workflows.
- Build and maintain reusable project templates, libraries, and CI/CD pipelines for ML projects.
- Automate model packaging, testing, deployment, and release processes using GitHub Actions or similar CI/CD tools.
- Develop deployment pipelines for machine learning models using Docker and cloud platforms (AWS preferred).
- Implement automated testing, including data validation, schema validation, model validation, and integration testing.
- Manage ML artifacts, model versions, and datasets using cloud storage solutions such as Amazon S3.
- Collaborate with Data Scientists to simplify project onboarding and improve development productivity.
- Monitor deployed models, troubleshoot production issues, and optimize deployment performance.
- Ensure security, governance, and compliance across ML deployment pipelines.
- Support multi-language ML environments (Python and R).

Required Skills

- 3–5 years of experience in MLOps, DevOps, or Machine Learning Engineering.
- Strong programming skills in Python.
- Experience with Git, GitHub, and GitHub Actions.
- Hands-on experience with Docker and containerized deployments.
- Experience with REST APIs (FastAPI)
- Experience working with AWS Cloud, including services such as:
- Amazon S3
- AWS IAM
- DynamoDB
- ECR




- ECS
- EKS
- Lambda
- RDS

- Experience designing and implementing CI/CD pipelines.
- Knowledge of ML model lifecycle management and deployment best practices.
- Experience in model packaging (e.g., Python wheels), dependency management, and virtual environments.
- Understanding of automated testing frameworks and validation pipelines.
- Robust debugging and troubleshooting skills.

Preferred Skills

- Experience with MLflow, Kubeflow, SageMaker, or similar MLOps platforms.
- Knowledge of Kubernetes and orchestration tools.
- Experience with Infrastructure as Code (Terraform or CloudFormation).
- Familiarity with monitoring and logging tools such as Grafana, Prometheus, or CloudWatch.
- Experience supporting Python machine learning workloads.
- Understanding of software engineering best practices, including code reviews and version control.

Qualifications

- Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, or a related field.
- Relevant AWS or DevOps certifications are a plus.

What You'll Do

- Build production-ready ML deployment frameworks.
- Automate model lifecycle management.
- Enable Data Scientists to focus on model development instead of infrastructure.
- Improve deployment speed, consistency, and reliability through automation.
- Create scalable and reusable MLOps solutions that support enterprise AI initiatives.

Nice to Have

- Experience with Generative AI/LLM deployment pipelines.
- Familiarity with Agile/Scrum development methodologies.
- Knowledge of security best practices for ML infrastructure and cloud deployments.

Roles and Responsibilities

What You'll Do

- Build production-ready ML deployment frameworks.
- Automate model lifecycle management.
- Enable Data Scientists to focus on model development instead of infrastructure.
- Improve deployment speed, consistency, and reliability through automation.
- Create scalable and reusable MLOps solutions that support enterprise AI initiative

📌 MLOps Engineer (Bengaluru)
🏢 In Time Tec
📍 Bengaluru