Job Description
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Company Overview
A technology company is building a data platform designed to support safe and fair decision-making. Its technology is used by more than 140,000 companies and millions of people for AI-powered verification across employment, housing, transportation, childcare, and other important areas.
Key Responsibilities
- Design, develop, and deploy machine learning models and AI systems used by production engineering teams.
- Build production-grade ML services including model code, API layers, monitoring systems, and automated tests.
- Integrate LLM APIs such as OpenAI and Anthropic into production applications and workflows.
- Determine when to use LLMs, fine-tuned models, classical machine learning approaches, or rules based on cost, latency, quality, and business requirements.
- Write clean, maintainable, well-structured production software using strong object-oriented programming principles, appropriate abstractions, error handling, and testing practices.
- Develop and maintain APIs that support production ML and AI services.
- Work with CI/CD pipelines and ensure software is properly tested, reviewed, deployed, and maintained.
- Partner with Product Engineering, Product, and cross-functional teams to translate business requirements into practical ML solutions.
- Define API contracts and communicate technical approaches clearly to technical and non-technical stakeholders.
- Build evaluation frameworks and conduct experiments to measure and improve model and system performance.
- Make data-driven decisions and iterate quickly based on evaluation results and production performance.
- Develop AI-powered workflows that automate internal processes and improve engineering and operational efficiency.
- Build agentic workflows and contribute reusable skills and context to broader AI platforms.
- Contribute to the organization’s broader AI strategy and the development of scalable AI systems.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field, or equivalent professional experience.
- 6+ years of professional software engineering experience.
- At least 2 years of experience building and operating machine learning systems in production.
- Strong proficiency in Python with the ability to write clean, testable, well-structured code.
- Strong understanding of object-oriented programming and software engineering principles.
- Hands-on experience integrating LLM APIs into production systems.
- Experience with prompt engineering, structured outputs, function calling, cost management, and LLM evaluation.
- Experience building and maintaining APIs.
- Experience working with CI/CD pipelines and production software delivery.
- Experience using AI-assisted development tools, LLMs, code-generation tools, or agentic systems in production or operational environments.
- Ability to evaluate AI-generated code critically and ensure production software meets engineering quality standards.
- Strong problem-solving and communication skills.
- Ability to work effectively in fast-moving environments and take ownership of end-to-end deliverables.
Preferred Qualifications
- Experience with MLOps platforms such as MLflow, SageMaker, Vertex AI, or similar technologies.
- Background in document processing, OCR, or information extraction.
- Experience with PySpark or large-scale data processing.
- Experience with Ruby or Ruby on Rails.
- Familiarity with compliance-sensitive industries such as fintech, legal technology, or HR technology.
- Working knowledge of dbt, Snowflake, or modern ELT and data transformation tools.
- Experience building scalable AI agents and agentic workflows.
- Experience developing machine learning systems for high-volume production environments.
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