Description

As a Senior AI Engineer, you will design and optimize AI-driven workflows, collaborate with product and data teams, and integrate advanced AI features into SaaS pricing applications, ensuring performance and reliability in a dynamic environment.

Requirements

• •Bachelor’s or master’s degree in Computer Science, Software Engineering, Business Information Systems, or related field
• •Programming background in Java and Python
• •Proven experience designing and implementing AI workflows and utilizing the LangChain framework
• •Deep knowledge of knowledge representation techniques and graph databases (e.g., Neo4j, TigerGraph)
• •Proficiency in building AI evals
• •Knowledge of MCP servers and multi-agent collaboration
• •Familiarity with monitoring and observability tools for AI (e.g. Braintrust or Arize)
• •Familiarity with ML/NLP Classifiers
• •Understanding of data pipelines and analytics workflows
• •5+ years of experience in software engineering, with at least 2+ years in an AI engineering role
• •Demonstrated ability to act hands-on with a can-do attitude to embed cutting-edge AI into high-impact pricing product domains
• •Experience working with agile methodologies and CI/CD pipelines
• •Experience in pricing, fintech, or B2B SaaS platforms
• •Familiarity with automation frameworks like n8n
• •Exposure to machine learning / optimization algorithms applied to pricing or similar domains

Tasks

• •Design and optimize interactions with LLMs using retrieval-augmented generation (RAG)
• •Implement tool-calling patterns to integrate LLMs with SaaS tools and APIs
• •Leverage fine-tuning techniques for performance enhancement
• •Manage prompts and chats, ensuring LLM responses align with user permissions
• •Log results and metadata for prompt and chat management
• •Utilize ontology-driven tagging to build semantic understanding via knowledge graphs
• •Develop a reinforcement learning layer to capture user feedback for decision-making models
• •Create adaptive decision support for AI agents to refine recommendations
• •Implement monitoring and evaluation pipelines for AI model performance and reliability
• •Collaborate with the AI Product Manager to translate roadmap deliverables into technical solutions
• •Work with Data Scientists on experimentation and data-driven insights
• •Integrate AI components with backend and frontend engineers into the SaaS architecture
• •Run experiments and track performance metrics to optimize AI LLM model behavior
• •Build tooling for prompt versioning, testing, and deployment pipelines

Tools & Technologies

Benefits