Senior Applied AI Engineer
New
A
AboonRetirement plan administration
This role is open to candidates based in the US.Full-TimeSenior
SalaryThe base salary range for this role is $220,000 - $240,000. Total compensation also includes an annual bonus and equity.
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Job Details
- Experience
- 6+ years building and running production software
- Required Skills
- Python
Requirements
- Have 6+ years of experience building and running production software, with clear ownership of technical decisions and post-launch outcomes.
- Have shipped an LLM- or agent-powered system used in a real workflow, and be able to explain how its quality was measured and improved.
- Have experience testing system behavior with tests, evaluation datasets, production monitoring, or structured human review.
- Reason about state, retries, fallbacks, permissions, latency, and cost, not only model outputs.
- Be comfortable integrating with external systems and working with structured and unstructured data.
- Have led the technical design of a significant system and produced design documents and code reviews relied on by other engineers.
- Be able to scope practical solutions to unclear problems, explain tradeoffs, and follow through on implementation and iteration.
- Work effectively with non-technical experts and communicate clearly in design documents, code reviews, and explanations of technical tradeoffs.
- Experience with document extraction, OCR, images, or other non-text inputs is a plus.
- Experience with multi-step agent workflows, tool use, or automation with human review is a plus.
- Experience in a regulated or high-trust domain, or owning systems at an early-stage company, is a plus.
- Experience with Python, Agno, or Anthropic models on AWS Bedrock is helpful but not required.
Responsibilities
- Extract and validate structured data from census files, plan documents, PDFs, and spreadsheets.
- Build agents that use tools and external services, track state, recover from failures, and hand off to a person when needed.
- Create evaluation datasets, regression tests, and production monitoring to track quality and identify problems.
- Choose between models, deterministic code, retrieval, and human review for individual workflow steps, and explain the tradeoffs.
- Improve production systems by reducing failures, latency, and cost, and making them easier to understand and debug.
- Write design documents, review code, and help engineers build and test AI features.
- Work with plan administrators and retirement plan experts to understand decision-making and translate it into workflows and agents.
- Own systems from design through evaluation, deployment, and ongoing production improvement.
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