Machine Learning Production Specialist

2 days ago

Bengaluru, Karnataka, India beBeeDeployment Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
Job Description

As an Artificial Intelligence Deployment Engineer, you will play a pivotal role in bridging the gap between machine learning development and production deployment. This involves collaborating closely with data scientists, software engineers, and business stakeholders to ensure that AI models are deployed, monitored, and scaled effectively.

This role requires a unique blend of technical skills in cloud infrastructure, CI/CD, and DevOps, as well as a deep understanding of the full machine learning lifecycle. You will be responsible for designing, developing, and implementing CI/CD pipelines for automating the deployment of machine learning models to production environments.

You will also be working on building tools to monitor model performance in real-time and triggering automatic re-training when model drift occurs. Furthermore, you will ensure models are scalable and optimized for high-performance.

Additionally, this role involves close collaboration with data scientists and software engineers to ensure smooth integration of AI models into production, focusing on reliability and scalability.

Automation is key to improving efficiency and reducing manual overhead, which means you will need to automate model training, deployment, and lifecycle management processes.

Cloud & Infrastructure Management:

  • Manage cloud resources (AWS, GCP, Azure) and infrastructure to ensure high availability, cost optimization, and efficient scaling of AI workloads.

Versioning & Experiment Tracking:

  • Implement versioning systems for model artifacts, datasets, and training environments.
  • Track experiments and ensure reproducibility.

Security & Compliance:

  • Ensure that the deployed models meet security and regulatory requirements (data privacy, model governance, etc.).

Troubleshooting & Optimization:

  • Debug and troubleshoot production AI models.
  • Optimize performance issues related to compute, data pipelines, and model inference.

Required Skills & Qualifications

Education:

  • Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field.

Experience:

  • 3+ years of experience in deploying and maintaining AI models in production environments.

Technical Skills:

  • Proficiency in programming languages such as Python, R, or Java.

Benefits

Collaborative environment where professionals from diverse backgrounds come together to innovate.

Opportunities for growth and professional development through continuous education and skill-building programs.

Competitive compensation package including salary and benefits.

Flexible work arrangements and remote work options available.

Recognition and rewards for outstanding performance and contributions.

Others

Preferred Qualifications:

  • Advanced ML Tools: Knowledge of tools like Kubeflow, Seldon, or TFX for automating ML pipelines.
  • Data Security: Understanding of secure deployment practices for machine learning models, including data privacy and compliance standards (GDPR, HIPAA, etc.).
  • Model Interpretability: Familiarity with techniques for explaining and interpreting machine learning models (e.g., SHAP, LIME).

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