• AI Agent Development: Design, build, and deploy AI-powered agents for applications such as virtual assistants, customer service bots, and task automation systems using LLMs and other AI models.
• Knowledge Graph Implementation: Develop and implement knowledge graphs for enterprise data integration, enhancing the retrieval, structuring, and management of large datasets to support decision-making.
• AI-Driven Process Optimization: Collaborate with business units to optimize workflows using AI-driven solutions, automating decision-making processes and improving operational efficiency.
• AI Ops (MLOps): Implement robust AI/ML pipelines that follow CI/CD best practices to ensure continuous integration and deployment of AI models across different environments.
• Model Monitoring and Maintenance: Establish processes for real-time model monitoring, including tracking performance, drift detection, and accuracy of models in production environments.
• Model Retraining and Optimization: Develop automated or semi-automated pipelines for model retraining based on changes in data patterns or model performance. Implement processes to ensure continuous improvement and accuracy of AI solutions.
• Cloud and ML Platforms: Utilize platforms such as Azure Machine Learning Studio, Google Vertex AI, and open-source frameworks for end-to-end model development, deployment, and monitoring.
• Collaboration: Work closely with data scientists, software engineers, and business stakeholders to deploy scalable AI solutions that deliver business impact.
• MLOps Tools: Leverage MLOps tools for version control, model deployment, monitoring, and automated retraining processes to ensure operational stability and scalability of AI systems.
• Performance Optimization: Continuously optimize models for scalability and performance, identifying bottlenecks and improving efficiencies.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 3+ years of experience as an AI Engineer, focusing on AI-powered agent development, knowledge graphs, AI-driven process optimization, and MLOps practices.
• Proficiency in working with large language models (LLMs) such as GPT-3/4, GPT-J, BLOOM, or similar, including both open-source and commercial variants.
• Experience with knowledge graph technologies, including ontology design and graph databases (e.g., Neo4j, AWS Neptune).
• AI Ops/MLOps Expertise: Hands-on experience with AI/ML CI/CD pipelines, automated model deployment, and continuous model monitoring in production environments.
• Familiarity with tools and frameworks for model lifecycle management, such as MLflow, Kubeflow, or similar.
• Strong skills in Python, Java, or similar languages, and proficiency in building, deploying, and monitoring AI models.
• Solid experience in natural language processing (NLP) techniques, including building conversational AI, entity recognition, and text generation models.
• Model Monitoring & Retraining: Expertise in setting up automated pipelines for model retraining, monitoring for drift, and ensuring the continuous performance of deployed models.
• Experience in using cloud platforms like Azure Machine Learning Studio, Google Vertex AI, or similar cloud-based AI/ML tools.

Preferred Skills:

• Experience with building or integrating conversational AI agents using platforms like Microsoft Bot Framework, Rasa, or Dialogflow.
• Familiarity with AI-driven business process automation and RPA integration using AI/ML models.
• Knowledge of advanced AI-driven process optimization tools and techniques, including AI orchestration for enterprise workflows.
• Experience with containerization technologies (e.g., Docker, Kubernetes) to support scalable AI/ML model deployment.
• Certification in Azure AI Engineer Associate, Google Professional Machine Learning Engineer, or relevant MLOps-related certifications is a plus.