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.

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.