Machine Learning Operations (MLOps Engineer)

Please find the below JD:

Job Title: MLOps Engineer
We are seeking an experienced MLOps Engineer with a minimum of 2 years of hands-on experience in deploying, scaling, and optimizing machine learning models. The ideal candidate should possess a strong background in Python programming, expertise in at least one backend framework (such as FastAPI, Flask, Tornado etc.), and a proven track record in Natural Language Processing (NLP), with a preference for experience in Large Language Models (LLMs).

Responsibilities:

ML Model Deployment and Scaling:
1. Deploy and manage machine learning models in production environments.
2. Develop and implement strategies for scaling models to handle varying workloads.
3. Monitor and optimize model performance and resource utilization.
4. Understand and build CI/CD pipelines.
5.Proficient in building machine learning pipelines.
6.Strong understanding of Kubernetes.
7.Building data pipeline with apache spark
8.Knowledge of databricks will be a plus

Python Programming:
5. Leverage strong Python programming skills for the development, integration, and maintenance of MLOps workflows.
6. Collaborate with software engineers to bridge the gap between model development and production deployment.

Backend Framework Expertise:
7. Demonstrate proficiency in at least one backend framework (e.g., FastAPI, Flask, Tornado) for building scalable and efficient web services.
8. Work on designing and implementing APIs to facilitate the integration of machine learning models into production systems.

Natural Language Processing (NLP):
9. Apply strong knowledge and practical experience in Natural Language Processing techniques.

Large Language Models (LLMs) (Bonus):
10. Familiarity or hands-on experience with Large Language Models is a plus.

Qualifications:
Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field.
Minimum of 2 years of experience as an MLOps Engineer.
Strong expertise in Python programming.
Proficiency in at least one backend framework (FastAPI, Flask, Tornado, Ruby, etc.).
Demonstrated experience in ML model deployment, scaling, and optimization.
Strong knowledge and practical experience in Natural Language Processing.
Experience in deploying ML models and applications on cloud technologies (AWS,GCP)
Bonus: Experience with Large Language Models (LLMs).