AI Engineer

Who We Are

We’re a team that believes in building intelligent systems that solve real business problems. Our engineering culture values experimentation, thoughtful system design, and practical innovation.

We focus on turning emerging AI capabilities into reliable, production-ready features that improve both internal workflows and customer-facing product capabilities. Our environment encourages engineers to explore ideas, test quickly, and build scalable solutions that deliver real impact.

If you're excited about designing AI workflows, working with structured data, and building intelligent product features, this role could be a great fit.

The Role

We are hiring an AI Engineer to help design, build, and improve AI-powered workflows and intelligent product capabilities.

This role is ideal for someone who combines strong hands-on experience in prompt development and optimization, agent chaining, and AI workflow design with the ability to work effectively with structured data, SQL, and dataset preparation.

The role also includes supporting analytics-oriented use cases, such as preparing data for reporting, building cohort logic, and contributing to forecasting and advanced analytical features.

This is a practical engineering role focused on turning AI capabilities into reliable, production-ready workflows. You will work across prompts, agents, structured datasets, and analytical logic to help build useful AI features for both internal tools and product use cases.

Responsibilities

• Design, develop, and optimize prompts for AI workflows and product use cases
• Build and improve multi-step AI workflows, including chaining, orchestration, validation, and output refinement
• Develop agent-based flows that combine reasoning, structured inputs, business rules, and prior outputs
• Prepare, clean, and structure datasets for AI workflows, evaluations, and analytical use cases
• Use SQL to query, validate, transform, and analyze structured data
• Support data preparation for reporting, cohort analysis, forecasting-related use cases, and advanced analytics features
• Help define how AI systems consume context, business rules, and structured business data
• Improve AI output quality through testing, iteration, evaluation, and optimization
• Collaborate with engineering, product, and data stakeholders to translate business needs into practical AI-powered capabilities
• Document prompts, workflows, assumptions, and output logic in a clear and maintainable way
• Use modern AI development tools to accelerate implementation, experimentation, and iteration

Qualifications

Required

• 1–3 years of experience in AI Engineering, Machine Learning, Data Science, Analytics Engineering, or a related role
• Hands-on experience working with large language models, particularly in prompt development and optimization
• Experience building or working with agent workflows, chained AI pipelines, or orchestration flows
• Strong SQL skills and comfort working with structured datasets
• Strong ability to prepare and structure datasets for AI workflows, analytics, or reporting
• Strong Python programming skills
• Good understanding of structured data, data quality, and how data design affects AI and analytics output quality
• Ability to evaluate and improve AI workflow performance through testing and iteration
• Strong analytical thinking and problem-solving abilities
• Good communication and documentation skills

Preferred

• Experience with prompt evaluation, response quality testing, or workflow benchmarking
• Familiarity with AI orchestration frameworks, tool calling, retrieval systems, or workflow automation patterns
• Exposure to cohort analysis, forecasting concepts, or advanced analytics use cases
• Experience working with customer, transactional, or behavioral datasets
• Experience shipping AI-powered features into production
• Familiarity with AI-assisted coding tools as part of day-to-day engineering workflows

Candidate Profile

The ideal candidate is someone who enjoys building practical AI systems that combine language models, structured data, and business logic.

They should be comfortable:

• Designing and refining prompts and AI workflows
• Working with structured datasets and SQL
• Evaluating and improving AI output quality
• Translating product or business needs into working AI features

You enjoy experimentation, iteration, and building systems that turn AI capabilities into reliable, useful product functionality. 

‍