Job Summary
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.
- Integrate APIs with API Gateway for authentication, rate limiting, and monitoring
- Develop web applications using Next.js
- Integrate frontend with chatbot APIs (Python / Node.js backend)
- 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).
- Design and develop chatbot solutions using Large Language Models (LLMs)
- Implement Retrieval-Augmented Generation (RAG) using knowledge bases
- Design and implement AI agent workflows with multi-step reasoning and decision-making
- Integrate chatbot with internal/external tools via function calling / API orchestration
- Manage conversation context, memory, and state across interactions
- Collaborate with data scientists to deploy trained models into production environments.
- Apply MLOps principles for continuous integration and delivery of AI models.
- Deploy and manage chatbot and AI services on Azure Cloud
- Work with services such as Azure OpenAI, Azure AI Search, App Service, and Storage
- Integrate data pipelines with cloud storage (e.g., Azure Data Lake, Blob Storage) and compute resources.
- Implement CI/CD pipelines, containerization (Docker), and scalable deployment patterns
- 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 an AI Engineer, Software Engineer, or related field.
- Strong coding skills i.e. Python, NodeJS
- 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
