AI/ML Engineer

AI/ML Engineer

Key Responsibilities

• Develop and optimize ML models for 

  VoCF validation planning

  , leveraging historical and real-time failure data.
• Work with backend engineers to 

  integrate ML models

   into Python FastAPI services.
• Collaborate with frontend developers to support 

  data-driven visualization and recommendations

  .
• Deploy, monitor, and maintain ML models on 

  Google Cloud Platform (Vertex AI, Cloud Run, Cloud Build, GCP Data Services)

  .
• Implement 

  MLOps pipelines

   for model training, testing, deployment, and monitoring in production.
• Ensure 

  scalability, reproducibility, and automation

   of ML workflows.
• Collaborate with enterprise data teams for 

  data sourcing, cleaning, transformation, and integration

  .
• Continuously improve model accuracy, explainability, and performance.

Technical Skills Required

• AI/ML:

   Strong experience in model development, optimization, and deployment.

• MLOps:

   CI/CD for ML, containerization, model monitoring, automated retraining.

• Cloud:

   Google Cloud Platform (Vertex AI, Cloud Run, Cloud Build, Data Services).

• Data:

   Proficiency in SQL/NoSQL databases, enterprise data pipelines, and ETL.

• Programming:

   Python (ML frameworks such as TensorFlow, PyTorch, or Scikit-learn).

• DevOps:

   Knowledge of Docker, Kubernetes, and cloud-native deployments.

Qualification

• Bachelor’s or Master’s degree in 

  Computer Science, AI/ML, Data Science, or related field

  .

• 3–6 years of hands-on experience

   in AI/ML projects with cloud deployment.
• Proven ability to deliver 

  end-to-end ML solutions

   from data preparation to production.
• Strong understanding of 

  cloud-native applications

   and enterprise-scale integration.