The AI Engineer will design, develop, and integrate AI-powered solutions into software applications using APIs and cloud-native services. With a strong foundation in software engineering and hands-on experience with Azure Cloud, this role focuses on operationalizing AI models, building intelligent features, and ensuring seamless deployment and scalability of AI services. The engineer will collaborate with product teams, data scientists, and cloud architects to deliver robust, secure, and efficient AI-driven systems.
Job Description
- Design and consume RESTful APIs for AI services, including generative AI, TTS, STT and NLP.
- Integrate third-party AI APIs (e.g., Azure OpenAI) into enterprise applications.
- Handle authentication, rate limiting, and error handling for AI API interactions.
- Document and maintain API specifications for internal and external use.
- Collaborate with data scientists to deploy trained models into production environments.
- Implement model inference pipelines and monitor performance metrics (latency, accuracy, throughput).
- Understand and apply machine learning algorithms such as K-Nearest Neighbors (KNN) for:
o Classification tasks (e.g., customer segmentation, fraud detection)
o Recommendation systems (e.g., product or content suggestions)
o Anomaly detection (e.g., identifying outliers in data)
- Evaluate KNN limitations (e.g., scalability, sensitivity to noise) and propose alternatives when appropriate.
- Collaborate with data scientists to deploy trained models into production environments.
- Apply MLOps principles for continuous integration and delivery of AI models.
- Architect and implement scalable infrastructure to support data science workflows, including:
o Data ingestion, preprocessing, and storage
o Model training environments with GPU/CPU clusters
o Experiment tracking and version control (e.g., MLflow, DVC)
- Enable collaborative environments for data scientists using tools like JupyterHub, Azure Machine Learning Studio, or Databricks.
- Integrate data pipelines with cloud storage (e.g., Azure Data Lake, Blob Storage) and compute resources.
- Ensure responsible AI practices, including fairness, explainability, and data privacy.
- Contribute to internal knowledge sharing and best practices for applied machine learning.
- Implement secure and scalable cloud architectures for AI model hosting and inference.
- Monitor and optimize cloud resource usage, ensuring cost-efficiency and performance.
- Use Azure DevOps for pipeline automation, model versioning, and release management.
Qualification
- At Least 3 years of experience as a Data Scientist, Software Engineer, or related field.
- Strong coding skills i.e. Python, SQL, pySpark
- Experience with CI/CD pipelines, Docker, Kubernetes, and cloud platforms (AWS, Azure, GCP).
- Hands-on experience with Azure Cloud services, especially those related to AI, data science, and DevOps.
- Experience designing and managing data science infrastructure, including compute, storage, and orchestration.
- Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Azure ML pipelines).
- Effective communication and teamwork to bridge technical and business needs.
- Continuous learning to adapt to evolving Data and AI technologies
