Engineer Jobs

About The Position

Requirements

• 5+ years software engineering; 1+ years focused on GenAI/LLM applications in production
• Has shipped AI features to real users - not just prototypes or internal demos
• Hands-on with LLM APIs in production - Anthropic Claude and/or OpenAI
• Built and shipped RAG systems to production (not just experimented with them)
• Experience with agentic frameworks - Agno, LangChain, LlamaIndex, or comparable
• Strong Python proficiency
• Azure or AWS cloud experience
• Vector databases - Pinecone, Weaviate, pgvector, or comparable
• Active user of AI coding assistants in daily workflow

Nice To Haves

• Financial services or data domain background - understanding of financial data schemas, transformation logic, or data quality requirements adds significant context to the role
• Experience with Temporal or workflow orchestration systems
• Experience fine-tuning models or working with open-source LLMs for domain-specific tasks
• Open-source AI contributions or technical writing
• Data engineering familiarity - understanding ETL/ELT patterns helps in building more effective transformation generation features

Responsibilities

• Build and iterate on AI-powered product features - transformation generation from natural language, integration configuration suggestions, data quality detection, and automated validation
• Implement LLM pipelines that are robust, observable, and production-ready - not proof-of-concepts
• Build and optimize LLM pipelines including prompt engineering, context management, and RAG systems tailored to financial services data schemas
• Evaluate and improve AI output quality continuously - build evaluation datasets and automated scoring frameworks
• Optimize for accuracy, latency, and cost across different usage patterns
• Implement multi-step agentic workflows using Agno,
• Build workflows that handle complex, multi-turn AI interactions cleanly - with proper error handling, retry logic, and human escalation paths
• Work closely with Backend Engineers to integrate AI capabilities into the platform's API and workflow layer
• Build the service interfaces that connect AI outputs to platform execution cleanly
• Participate in code reviews and maintain high standards for code quality and testability

Benefits

• Discretionary bonuses
• Profit-sharing
• Long-term savings
• Healthcare
• Income protection
• Professional development opportunities
• Time off