MLOps Engineer

Position : MLOps Engineer

Key Responsibilities:

 Take ownership of the ML deployment pipeline. An ideal candidate would be the

point person for anything related to ML Deployment. The MLOps Engineer would lead

the design, development, and execution of our deployment infrastructure.

 Design and implement deployment strategies for various tools and ML models

with consideration of scalability, cost, and ease of use. An ideal candidate would be

able to communicate and educate the team on the design decisions, alternatives

considered, and how certain strategies would affect our ML deployments in the future.

Current technologies that would require a deployment strategy include:

 Docker containers

 Pytorch models

 XGBoost / scikit-learn models

 Ensure the security of proprietary work and manage access policies to our IP. As a

pharmaceutical company, most, if not all, of our work is trained on proprietary data. We

want to ensure that our solutions aren't accessible outside of our organization, but we

also want teams that we collaborate with internally to have easy access to these

solutions. An ideal candidate prioritizes security in the strategies they design and

implement.

 Automate testing, enforce code quality, and apply development best practices. As

the team works on multiple projects at a fast pace, we encounter pitfalls in methods or

code weve developed but havent tested rigorously. An ideal candidate would help

establish standards for best practices in coding that would make deployment easier in

the future, less prone to failures, and empower the team to create solutions rapidly.

 Collaborate with the team and be proactive with improvements. As the team builds

and creates solutions for various groups within the company, we often forget to pause

and understand gaps in our team's knowledge or areas where we could improve. An

ideal candidate would be able to collaborate with the team on strategies to improve our

infrastructure while navigating project deadlines. An ideal candidate would also help

empower other team members to understand and apply development practices that

contribute to the development of the ML deployment pipeline.

 Be open to learning and open to teaching. The MLOps Engineer will be someone the

team relies on for model deployment. As technology evolves and our tools and models

change, the team must adapt to these changes efficiently. We dont expect the MLOps

Engineer to be an expert in model development, nor do they need a background in the

life sciences to be effective in this role.However, an ideal candidate would be open to

learning from others, just as we can rely on their expertise.

Basic Qualifications:

 Experience in MLOps or a similar role, with proven experience in deploying machine

learning models to production

 Experience in designing, building, and managing MLOps pipelines

 Experience in cloud computing, particularly with AWS

 Experience in building and managing API endpoints

 Experience with MLOps tools such as MLFlow, KubeFlow, or AirFlow

 Familiarity with ML tools and frameworks such as pandas, numpy, scikit-learn, and

PyTorch

 Experience with Infrastructure as Code tools such as CloudFormation or Terraform

 Strong problem-solving skills and the ability to work independently or collaboratively

Nice to Have:

 Strong experience with container management and deployment, such as using

Kubernetes, AWS, ECR, AWS Fargate, AWS Batch

 Prior experience in building an end-to-end MLOps pipeline for deep learning models

from the ground up

If interested, please share your profile along with the following details to hidden_email and hidden_email.

1. Total work experience:

2. Notice Period:

3. Current Annual package

4. Expected Annual Package:

5. Ready for Coimbatore location Y/N:

6. Do you have any offers in hand  

Best Regards

Abinaya S,