The company is a pioneering AI technology company designed to innovate and excel within regulated industries such as healthcare, insurance, government, and financial services. Our flagship platform, Opus, automates and supervises mission-critical, document-heavy processes while ensuring governance, auditability, and human oversight.
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The AI Workflow Engineering Architect will serve as the senior technical authority responsible for designing and optimizing production workflows within Opus, particularly for ML-intensive and computationally demanding business processes. This role involves defining how workflows should be engineered to achieve the required accuracy and reliability with minimal inference cost and latency. Key responsibilities include leading the technical development of Workflow Engineers, establishing standards and practices in workflow engineering, and converting advances in ML and computer science into reusable engineering patterns.
Key Responsibilities:
- Workflow Architecture and Optimization: Develop efficient Opus workflow graphs, formulating important workflow design decisions.
- Model Architecture, Selection, and Routing: Define methods for selecting and composing models within Opus with data-driven decisions.
- Evaluation and Experimental Methodology: Set engineering standards for workflow demonstrations and establish testing and evaluation practices.
- Inference and Systems Performance: Conduct technical analysis to improve workflow execution cost and performance.
- Reliability and Execution Semantics: Implement best practices for reliable workflow execution and educate Workflow Engineers on failure behavior.
- Production Measurement and Continuous Optimization: Define workflow performance metrics and feedback mechanisms to guide engineering decisions.
- Technical Leadership and Engineering Capability: Mentor Workflow Engineers, lead technical designs, and ensure adherence to engineering standards.
Required Skills:
- Senior-level applied ML and software engineering experience, particularly in operating production LLM/agentic systems.
- Expertise in ML evaluation methodologies and systems performance.
- Familiarity with distributed systems principles, including reliability and failure handling.
- Proven experience in mentoring engineers and setting technical standards.
Desirable Skills:
- Experience with multi-model orchestration and background in regulated industries.
- History of establishing engineering standards or technical disciplines in early-stage environments.
- Publications or patents related to ML systems or optimization are a plus.
What We Offer:
- Opportunity to shape the marketing analytics strategy at a leading AI company.
- Exposure to cutting-edge AI/ML applications in real-world business contexts.
- Collaborative and innovative work environment.
- Competitive compensation, benefits, and career advancement opportunities.
