MLOps Engineer - Python/SQL (8-16 yrs)

Candidate is expected to productionalize & deploy the model code (base code will be developed by Data Science). This role will have to spend good amount of time in Scaling the pre-developed model training and inference (multi-GPU/CPU). We expect the candidate to have Strong Python/SQL , Kubernetes, Scaled Model deployment and ML experience.

Here is a more detailed JD I generated via AI

About the Role :

We are seeking a highly skilled MLOps Engineer to play a pivotal role in bringing cutting-edge AI models to production. You will collaborate closely with our Data Science and Machine learning teams to optimize, scale, and deploy AI/ML models.

Responsibilities :

Model Productionization and Deployment :

- Translate complex machine learning models into robust and scalable production systems.

- Deploy models to production environments using Kubernetes or other container orchestration tools.

- Ensure seamless integration of models with existing infrastructure and applications.

Performance Optimization :

- Identify and implement strategies to optimize model training and inference performance.

- Leverage techniques like GPU acceleration, distributed training, and model quantization to improve efficiency.

- Monitor model performance in production and proactively address any performance bottlenecks.

Scalability Engineering :

- Design and implement scalable solutions for handling large-scale data and model workloads.

- Optimize data pipelines and model serving infrastructure to meet growing demands.

- Collaborate with infrastructure teams to ensure adequate resources and capacity.

ML Operations (MLOps) :

- Establish and maintain robust MLOps practices to streamline model development, deployment, and monitoring.

- Implement automated pipelines for model training, testing, and deployment.

- Monitor model performance in production and take corrective actions as needed.

Qualifications :

- Strong proficiency in Python programming language and SQL.

- In-depth knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).

- Expertise in Kubernetes or other container orchestration tools.

- Experience with cloud platforms (e.g., AWS, GCP, Azure).

- Solid understanding of distributed computing and parallel programming.

- Strong problem-solving and analytical skills.

- Excellent communication and collaboration abilities.

Preferred Qualifications :

- Experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow).

- Knowledge of big data technologies (e.g., Hadoop, Spark).

- Experience with model optimization techniques (e.g., pruning, quantization, distillation).