Ai Engineer

Overview

• Collaborate with corporate teams to design and implement AI solutions improving decision-making and automating workflows.
• Develop machine learning models tailored to structured and unstructured data for diverse business applications.
• Optimize AI processes, including prompt engineering and retrieval strategies, ensuring high performance and relevance.
• Engage stakeholders to identify impactful AI use cases and deliver measurable business outcomes.
• Experiment with emerging AI techniques to enhance productivity and reduce manual efforts.
• Ensure compliance with responsible AI practices, including data privacy and governance.
• Document technical processes and solutions to support scalability and reuse across teams.
• Provide technical guidance and mentorship on AI best practices within the organization.

Key Responsibilities & Duties

• Design and deploy AI-driven solutions enhancing business processes and user productivity.
• Develop machine learning models for practical applications, ensuring high accuracy and relevance.
• Optimize AI workflows, including embeddings and retrieval strategies, for improved outcomes.
• Collaborate with stakeholders to translate business needs into effective AI solutions.
• Experiment with new AI methodologies to innovate and streamline operations.
• Maintain responsible AI practices, including transparency and governance.
• Document AI models and processes for scalability and team collaboration.
• Provide expertise on AI technologies and practices to support organizational goals.

Job Requirements

• Bachelor of Science degree in a relevant field is required.
• Minimum of 3 years of experience in AI or machine learning development, with 5 years preferred.
• Proficiency in Python programming and experience with Azure OpenAI or similar platforms.
• Expertise in generative AI, prompt engineering, and retrieval-augmented generation.
• Strong understanding of embeddings, vector search, and semantic retrieval techniques.
• Experience with structured and unstructured data, including text-heavy datasets.
• Knowledge of MLOps practices, model versioning, and monitoring in production environments.
• Microsoft or AI-related certifications are beneficial.