MLOps Engineer

7 days ago

HyderabadBangaloreChennaiPune, India ARR Recruitment Solutions Full time

Client name - Tech mahindra

We are seeking a highly skilled and experienced MLOps Engineer to join our growing team.

You will play a critical role in bridging the gap between machine learning (ML) development and production by building and maintaining robust pipelines for deploying, monitoring, and managing ML models at scale.

Leveraging your expertise in Google Cloud Platform (GCP) and MLOps best practices, you will ensure the efficient and reliable operation of our ML models in production.

Educational Background:

- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

Technical Skills:

GCP Expertise:

- Proficiency in Google Cloud Platform (GCP) services, particularly:

1. AI Platform (model training, deployment, and management)

2. BigQuery (large-scale data storage and querying)

3. Cloud Storage (scalable object storage)

4. Google Kubernetes Engine (GKE) (container orchestration for ML workloads)

Machine Learning and Programming:

- Strong programming skills in Python, with experience in popular ML frameworks like TensorFlow, PyTorch, or Scikit-learn.

- Familiarity with generative AI models and tools from Google's Model Garden (a plus).

Automation and CI/CD:

- Experience with CI/CD tools like Jenkins, GitLab CI, or Cloud Build to automate ML pipeline deployment and testing.

- Knowledge of containerization and orchestration tools, especially Docker and Kubernetes.

Monitoring and Observability:

- Familiarity with monitoring and logging tools like Stackdriver, Prometheus, or Grafana to track model performance and identify potential issues.

Preferred GCP Professional Data Engineer or Professional Machine Learning Engineer certification (a plus).

Kubeflow Experience:

- Hands-on experience with Kubeflow Pipelines for managing and scaling ML workflows (a plus).

Generative AI Expertise:

- Specific experience deploying and managing generative AI models in production (a plus).

Data Engineering Skills:

- Familiarity with data engineering concepts and tools, such as Apache Beam or Dataflow (a plus).

Domain Knowledge:

- Experience in the specific domain of the enterprise (e.g, finance, healthcare, retail) is a Design and implement robust MLOps pipelines for training, deploying, and managing ML models in production on GCP.

- Collaborate with data scientists and software engineers to ensure seamless integration of ML models into the application lifecycle.

- Automate ML workflows using CI/CD tools and containerization technologies.

- Monitor model performance, identify potential issues, and implement corrective actions.

- Continuously optimize MLOps processes to improve efficiency, scalability, and reliability of ML deployments.

- Stay up-to-date with the latest advancements in MLOps tools and technologies

(ref:hirist.tech)

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