Own the agent layer of the agentic framework — the build, not the design. Run the agent development pipeline: LLM integration, prompt-to-action engineering, agent evaluation and testing, and the reusable agent components the rest of the function depends on.
Locations (3)
United Kingdom
India
United States
Responsibilities
Own the agent layer: agent development pipeline, LLM integration, prompt-to-action engineering and agent lifecycle.
Own agent evaluation and testing, including credible approaches to non-deterministic output.
Build GenAI security use cases such as alert triage automation and threat summarisation.
Develop reusable agent components that reduce effort on the next build.
Build to AI Architecture's specifications and raise design problems rather than engineering around them.
Requirements
Bachelor's degree in a relevant discipline, or equivalent professional experience.
Compensation Range:
Pay Range - Start:
$118,960.00
Pay Range - End:
$178,440.00
Geographic Specific Pay Structure:
Structure 110:
$130,880.00 USD - $196,320.00 USD
Structure 115:
$136,800.00 USD - $205,200.00 USD
We believe in fairness and transparency. It’s why we share the salary range for most of our roles. However, final salaries are based on a number of factors, including the skills and experience of the candidate; the current market; location of the candidate; and other factors uncovered in the hiring process. The standard pay structure is listed but if you’re living in California, New York City or other eligible location, geographic specific pay structures, compensation and benefits could be applicable, click here to learn more.
Responsibilities
Applies and integrates advanced statistical, mathematical, data mining, predictive modeling and business analysis skills and techniques to discover and identify new insights, predict and prescribe outcomes, advise on turning new insights into actionable opportunities and outcomes, and provide the tracking and measurement of results.
Develop LLM-based, mathematical models, algorithms, machine learning techniques, and robust analytics to support analytic insights and visualization of complex data sets for productization and monetization.
Create evaluation frameworks and monitor deployed models and AI systems for performance, drift and unintended outcomes.
Applies creative problem solving and inter-departmental networking to address data and information needs across the enterprise.
Shares best practices with peers through coaching and mentoring core methodologies, patterns, standards and processes.