Ai Engineer

Overview

• Design and deploy advanced AI systems to solve complex business challenges and drive innovation.
• Collaborate with multidisciplinary teams to identify AI opportunities and transform them into impactful solutions.
• Mentor and guide engineering teams, fostering growth and enhancing technical capabilities.
• Develop scalable and efficient AI/ML solutions leveraging cutting-edge technologies and cloud infrastructure.
• Implement robust evaluation frameworks to ensure continuous improvement and reliability of AI systems.
• Contribute to the advancement of AI technologies in compliance-sensitive and high-impact domains.
• Operate in a hybrid work environment, balancing in-office collaboration and remote flexibility.
• Utilize expertise in LLM systems, document intelligence, and ML platform design to innovate solutions.

Key Responsibilities & Duties

• Architect and implement AI systems focused on workflow automation and document intelligence.
• Lead technical design and decision-making processes to align with organizational goals and best practices.
• Collaborate with stakeholders to ensure AI projects meet business objectives and deliver measurable outcomes.
• Develop and maintain ML pipelines for reliable and scalable AI system deployment.
• Establish evaluation metrics and frameworks to monitor and improve AI system performance.
• Enhance team processes and infrastructure to optimize development efficiency and reduce technical debt.
• Ensure adherence to software engineering best practices, including CI/CD, testing, and documentation.
• Optimize cost and latency for large-scale AI inference using advanced methodologies.

Job Requirements

• Bachelor of Science degree in Computer Science, Engineering, or a related field.
• Minimum of 6 years of experience in developing and deploying production-grade AI/ML systems.
• Proficiency in Python and cloud-native development patterns for AI/ML workloads.
• Expertise in LLM systems, document intelligence, and scalable ML platform design.
• Strong knowledge of MLOps practices, including model training, deployment, and monitoring.
• Solid understanding of statistics, experimentation, and data quality principles.
• Proven ability to mentor engineers and lead technical design decisions effectively.
• Experience optimizing cost and latency for large-scale AI inference systems.