Ai Engineer

Overview

• Lead the design and delivery of production-grade AI systems to achieve measurable business outcomes.
• Collaborate with cross-functional teams to identify AI opportunities and translate requirements into technical solutions.
• Mentor engineers and drive architectural decisions to enhance team capabilities and system quality.
• Develop robust evaluation frameworks for AI systems ensuring reproducibility and continuous improvement.
• Operate in a hybrid work environment with flexibility for remote work on Fridays.
• Contribute to the advancement of AI technologies in compliance-sensitive domains.
• Engage in the development of scalable AI/ML solutions leveraging cloud-native patterns.
• Utilize expertise in LLM systems, document intelligence, and ML platform design.

Key Responsibilities & Duties

• Architect and deliver AI systems for workflow automation and document intelligence.
• Lead technical design, mentoring, and architectural decisions within the engineering team.
• Collaborate with stakeholders to align AI projects with business goals and communicate technical tradeoffs.
• Implement and monitor end-to-end ML pipelines ensuring reliability and scalability.
• Develop evaluation frameworks and metrics for AI systems to ensure continuous improvement.
• Enhance team processes and infrastructure to reduce technical debt and improve development velocity.
• Ensure adherence to software engineering best practices including CI/CD, testing, and documentation.
• Optimize cost and latency for LLM inference at scale using advanced techniques.

Job Requirements

• Bachelor of Science degree in a relevant field is required.
• Minimum of 6 years of experience in developing production AI/ML systems.
• Proficiency in Python and cloud-native development patterns for AI/ML workloads.
• Expertise in LLM systems, document intelligence, and ML platform design.
• Experience with MLOps, including model training, deployment, and monitoring.
• Strong fundamentals in statistics, experimentation, and data quality.
• Ability to mentor engineers and lead technical design decisions.
• Knowledge of cost and latency optimization for LLM inference at scale.