AI Software Engineer
In this Role, Your Responsibilities Will Be:
Apply diverse AI technologies (frameworks, libraries, models, algorithms) to solve business-specific software challenges
Use Azure OpenAI services and enterprise-grade AI platforms for solution development
Design, train, fine-tune, and integrate machine learning models into production applications
Implement LLM prompt engineering as a core skill for building intelligent solutions
Collaborate with data engineers and scientists to preprocess and analyze data for AI training and testing
Manage data pipelines and engineering tasks to leverage internal business data sources
Develop and deploy AI agents and automation solutions tailored to organizational, functional, and/or departmental activities
Integrate tools via APIs, database connectors, and RPA platforms for seamless workflows
Research, design, and deliver end-to-end AI solutions aligned with organizational goals
Stay current with emerging AI technologies and frameworks
Participate in scrum activities and collaborate with global teams to align technical solutions with business objectives
Utilize agent orchestration frameworks such as but not limited to (Autogen, CrewAI, LangChain Agents, Semantic Kernel) for scalable automation
Maintain and update technical documentation for clarity and scalability
For This Role, You Will Need:
Bachelor’s or Master’s degree or equivalent in Engineering, Computer Science, Artificial Intelligence, IT, or any related field.
Proven background in full-stack development
Strong experience with Python and modern AI/ML frameworks
Hands-on experience incorporating RAG systems and Large Language Models.
Strong analytical and problem-solving skills, with excellent communication and collaboration skills
Preferred Qualifications that Set You Apart:
Minimum of 2 years of experience in AI/ML projects with a solid understanding of AI application design and deployment.
Hands-on experience with Azure, Azure ML Studio, Azure Copilot, and GitHub Copilot for building and managing AI solutions.
Familiarity with AI/LLM frameworks such as Haystack, LangChain, Ollama, or similar technologies.
Strong knowledge of best practices including Agile methodologies, test-driven development (TDD), and DevSecOps principles.
Ability to integrate AI workflows into enterprise environments and leverage cloud-native services for scalability and security.