Senior Software Engineer, Full-Stack

About Doowii

Doowii is building a conversational analytics platform for education. We enable non-technical users to query complex datasets using natural language, accelerating decision making, data exploration, and intervention planning. Doowii sits at the intersection of data infrastructure, AI, and education.

We are seeking strong full-stack engineers who are proficient in web frameworks, backend development, and infrastructure. As an Applied AI Software Engineer, you'll help design, build, evaluate, and operate the AI-powered systems that transform natural language into actionable insights. Applicants must demonstrate technical mastery, architectural skills, mentoring abilities, and independence.

You'll work across Doowii's tech stack, from LLM orchestration and retrieval systems to backend APIs, data pipelines, and customer-facing product experiences.

Doowii engineering is critical in the end-to-end development of the Doowii platform, applications, and systems, from designing the user interface to managing our systems and infrastructure.

• Design and implement AI-powered product capabilities using large language models, embeddings, retrieval systems, and agent workflows
• Build and maintain evaluation frameworks to measure AI quality, accuracy, reliability, and customer impact
• Improve prompt strategies, tool usage, retrieval quality, and agent behavior
• Develop systems for semantic search, retrieval-augmented generation (RAG), and conversational analytics
• Experiment with new models, frameworks, and AI techniques to improve platform capabilities
• Partner with product and engineering teams to translate customer needs into AI-driven solutions

• Build scalable backend services and APIs that support AI workflows and customer-facing applications
• Design and maintain services that orchestrate LLM interactions, retrieval systems, and external tools
• Develop and optimize data processing workflows that support AI-powered experiences
• Improve observability, reliability, testing, and deployment practices for AI systems
• Contribute to architecture decisions across application, infrastructure, and data layers

• Build and maintain retrieval pipelines, embedding workflows, and vector search systems
• Design data models and indexing strategies that improve AI accuracy and performance
• Work with structured and unstructured datasets to support analytics and natural-language experiences
• Optimize storage, retrieval latency, and evaluation workflows

• Contribute to frontend and user-facing product features when needed
• Partner closely with frontend, backend, data, and product teams
• Help shape the user experience of AI-powered features and workflows

• Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, or related field (or equivalent practical experience)
• 3+ years of professional software engineering experience
• Strong proficiency in Python
• Experience building and maintaining production software systems
• Experience working with LLM APIs and modern AI application frameworks
• Experience implementing retrieval, embeddings, vector search, or RAG workflows
• Experience designing APIs and backend services
• Strong SQL and data modeling skills
• Experience with cloud platforms such as AWS, GCP, or Azure
• Experience evaluating AI system quality, reliability, or performance
• Strong problem-solving skills and comfort working across multiple technical domains

Bonus points if you have:

• Masters in computer engineering
• Experience in any of the following areas:
  • building agent-based systems or tool-calling workflows
  • developing LLM evaluation frameworks, automated testing, or benchmark systems
  • fine-tuning models or working with open-weight models
  • working with vector databases such as Pinecone, Weaviate, pgvector, Open Search, or equivalent
  • building analytics platforms or data-intensive applications
  • working with Airflow, Dagster, dbt, Kafka, Spark, Iceberg, Click House, Big Query, or Snowflake
  • managing complex workflows with prompt engineering, structured outputs, and AI safety/reliability techniques
  • working with conversational interfaces, search systems, or natural-language-driven products