MLOps Engineer
2 weeks ago
Location: Coimbatore/Remote
Notice Period : Immediate Joiners are Preferred
Experience: 8+ years
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 we’ve developed but haven’t 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 don’t 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 and
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
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