Overview :
Join a technology-driven team focused on deploying and evolving machine learning and artificial intelligence solutions to address dynamic business needs. Help design, build, and maintain advanced AI systems in modern cloud environments.
Key Responsibilities :
- Deploy, monitor, and manage ML / AI models in production environments.
- Collaborate closely with data engineers to design, implement, and maintain robust ML / AI pipelines and workflows.
- Continuously tune and retrain existing models to support evolving business requirements and use cases.
- Integrate AI models into production systems via APIs or microservices.
- Ensure security, reliability, scalability, and optimal performance for all deployed models.
- Evaluate and recommend models and tools tailored to specific business challenges.
- Contribute to advanced analytics initiatives, including graph analysis and related technologies.
- Design, build, and maintain AI agents and conversational chat applications for cloud-based platforms.
- Stay current with the latest AI research, trends, tools, and frameworks.
- Communicate complex AI concepts clearly to both technical and non-technical stakeholders.
- Champion and enforce compliance with ethical standards, data governance, and privacy regulations.
Required Qualifications :
Bachelors or Masters degree in Computer Science, Engineering, Data Science, or a related field.Proficiency in Python (preferred), Java, C++, or similar programming languages.Hands-on experience with deploying ML / AI models into production environments.Strong understanding of machine learning algorithms and the full model lifecycle.Familiarity with chatbots and AI agents, especially in cloud environments.Expertise in Natural Language Processing (NLP) techniques.Experience with API integration and designing microservices for conversational systems.Practical cloud platform experience, with a preference for Azure.Knowledge of software engineering best practices, including version control (e.g., Git).Preferred Skills :
Demonstrated success in production deployment of AI applications.Experience with graph analysis tools and techniques (e.g., Neo4j, NetworkX, graph databases).Familiarity with modern analytics and AI toolsets, such as Databricks, Azure AI Foundry, and CI / CD pipelines.Exposure to advanced AI applications such as large language models (LLMs), reinforcement learning, or real-time inference systems.