AI Engineer (Enterprise CloudML/AI)

5-7 Years

Save

Early Applicant

Job Description

ROLES AND RESPONSIBILITY

Design, train, and deploy AI/ML models, including offline deployment for enterprise use cases.
Integrate AI models into enterprise applications using cloud-native architectures.
Implement data pipelines, feature engineering, and model evaluation frameworks.
Optimize models for performance, scalability, and cost-efficiency.
Ensure security, compliance, and governance in AI solutions.
Train and mentor team members on AI/ML concepts, tools, and best practices.
Collaborate with product and engineering teams to deliver AI-driven features.

DESIRED SKILLS

5 years in enterprise software development with strong coding skills (e.g., Python, .NET, or Java).
2 years in ML/AI: model training, evaluation, and deployment.
Hands-on experience with enterprise cloud platforms (Azure, AWS, or GCP) and relevant AI services.
Proven experience in at least one end-to-end AI project involving offline model deployment.
Strong understanding of data preprocessing, feature engineering, and model optimization.
Familiarity with MLOps practices: CI/CD for ML, model versioning, monitoring.

GOOD TO HAVE

Experience with Azure AI Services, Azure ML, or equivalent cloud AI platforms.
Knowledge of vector databases, RAG architectures, and LLM integration.
Familiarity with containerization (Docker, Kubernetes) and IaC (Terraform/Bicep).
Exposure to Responsible AI principles and compliance frameworks.

TOOLS & TECHNOLOGIES