Founding AI Engineer (India)

About Fiska Fiska is transforming how finance teams manage event spend. We’re building the first AI-powered platform that automates expense tracking, budget management, and real-time reconciliation for social media and marketing professionals (SMMPs).

Our customers save an average of $250K+ annually and reduce reconciliation time by 78%. We’re backed by leading investors and working with global brands to solve billion-dollar problems in event finance.

About The Role As a Founding AI Engineer at Fiska, you’ll be at the cutting edge of building production-grade AI systems that power our intelligent expense management platform. You’ll own the entire AI stack—from research and experimentation to production deployment—and directly impact how thousands of finance teams interact with financial data.

This is a rare opportunity to shape the AI/ML direction of an early-stage startup, working directly with cofounders to build agentic systems, implement RAG pipelines, and create AI experiences that feel magical to users while maintaining enterprise-grade reliability and security.

Key Responsibilities

• Agentic Systems: Take agentic AI SMMP workflows from concept to production, owning the complete pipeline including retrieval, reasoning, tool integration, and safety measures while continuously improving accuracy through data-driven iteration
• Evaluation Infrastructure: Architect robust evaluation frameworks for prompts and models with both offline and online testing capabilities, enabling rapid experimentation through A/B testing and systematic performance comparison
• Context Engineering: Build sophisticated context management systems incorporating RAG, long-term memory, intelligent summarization, and data compression to enhance both conversations and automated expense categorization
• Product Collaboration: Partner directly with founders and product leaders to transform customer pain points into intelligent agent capabilities, run experiments, and deliver production features
• Internal Tools: Develop reusable tools and SDKs that empower engineering teams and AI systems to build faster, creating a multiplier effect across the organization
• Model Orchestration: Manage integrations across multiple LLM providers with intelligent fallback mechanisms, balancing cost efficiency, response speed, and output quality for optimal performance
• Observability: Implement comprehensive monitoring infrastructure tracking agent behavior, API costs, failure patterns, hallucination detection, and safety guardrails with actionable dashboards driving product strategy
• Safety & Compliance: Design and deploy robust guardrails, and implement safety protocols ensuring AI-generated outputs meet stringent financial industry compliance and regulatory requirements
• Continuous Improvement: Establish clear success metrics covering accuracy, latency, cost, and safety, design and execute A/B tests, and integrate user feedback loops to drive measurable quality improvements

Requirements

• Proactive and self-motivated engineer with strong ownership mentality and ability to drive projects independently from inception to completion
• Builder’s mindset with comfort navigating ambiguity, rapid prototyping skills, systematic debugging approach, and relentless focus on user experience
• Demonstrated expertise in large language models through professional work or exceptional personal projects showcased on your resume
• Proven experience integrating multiple LLM providers with tool-calling and function-calling capabilities, comfortable switching between models and implementing robust fallback strategies
• Deep hands-on experience with RAG systems including indexing strategies, chunking techniques, embeddings, and reranking, with understanding of reliability and cost-latency optimization
• Solid computer science fundamentals including data structures, algorithms, comprehensive testing practices, and performance profiling
• Advanced prompt engineering skills with experience building prompt libraries, analyzing failure modes, and systematically refining prompts and policies
• Strong analytical ability to establish meaningful KPIs covering accuracy, latency, cost, and safety, with experience designing A/B tests and implementing human feedback systems
• Production experience with MongoDB including schema design, optimization, and scaling
• Proficiency in Python with hands-on experience in modern ML/AI frameworks and libraries…