Cloud AI Engineer

Job Description

Key Responsibilities :

– Design, develop, and deploy AI/ML models in cloud environments (AWS, GCP, Azure).

– Build and manage end-to-end ML pipelines using cloud-native tools (e.g., SageMaker, Vertex AI, Azure ML).

– Collaborate with data scientists, engineers, and stakeholders to define AI use cases and deliver solutions.

– Automate model training, testing, and deployment using MLOps practices.

– Optimize performance and cost of AI/ML workloads in the cloud.

– Ensure security, compliance, and scalability of deployed AI services.

– Monitor model performance in production and retrain models as needed.

– Stay current with new developments in AI/ML and cloud technologies.

Required Qualifications :

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

– 3+ years of experience in AI/ML and cloud computing.

– Hands-on experience with cloud platforms (AWS, GCP, or Azure).

– Proficient in Python, TensorFlow, PyTorch, or similar frameworks.

– Strong understanding of MLOps tools and CI/CD for machine learning.

– Experience with containerization (Docker, Kubernetes).

– Familiarity with cloud-native data services (e.g., BigQuery, S3, Cosmos DB).

Preferred Qualifications :

– Certifications in Cloud (e.g., AWS Certified Machine Learning, Google Cloud Professional ML Engineer).

– Experience with generative AI, LLMs, or real-time inferencing.

– Knowledge of data governance and ethical AI practices.

– Experience with REST APIs and microservices architecture.

Soft Skills :

– Strong problem-solving and analytical skills.

– Excellent communication and collaboration abilities.

– Ability to work in a fast-paced, agile environment.

Skills : Machine Learning (ML), Amazon Web Services (AWS), Python, PyTorch and TensorFlow.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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