Senior AI Engineer

What You'll Do:

LLM & AI Pipeline Engineering

• Design, build, and maintain production-grade LLM integration pipelines — including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.
• Develop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.
• Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.
• Evaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.
• Establish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.

Retrieval & Vector Search

• Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.
• Build document ingestion and chunking pipelines for Jeeves's financial data — processing invoices, receipts, policy documents, and transaction records.
• Optimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.

ML Model Serving & Operations

• Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.
• Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.
• Implement model performance monitoring and data drift detection to ensure production models remain accurate over time.
• Support model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.

Backend Integration & Reliability

• Integrate AI services cleanly with Jeeves's backend microservices — designing clear API contracts, circuit breakers, and graceful degradation patterns.
• Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.
• Instrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.
• Build human-in-the-loop review workflows for AI decisions that require oversight — particularly for high-value financial actions.

Collaboration & Growth

• Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.
• Contribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.
• Help grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.

Requirements:

Minimum Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field — or equivalent practical experience.
• 5+ years of professional software engineering experience, with at least 3 years focused on AI/ML systems in production.
• Hands-on experience building and deploying LLM-powered applications using APIs such as OpenAI, Anthropic, or Cohere in a production environment.
• Experience designing and operating RAG pipelines, including chunking strategies, embedding models, and vector database integration (Pinecone, Weaviate, pgvector, or similar).
• Strong proficiency in Python for AI/ML workloads; familiarity with at least one AI orchestration framework (LangChain, LlamaIndex, or equivalent).
• Experience with ML model serving infrastructure: REST or gRPC inference endpoints, input/output validation, latency budgeting, and monitoring.
• Solid backend engineering fundamentals: REST APIs, relational databases (PostgreSQL preferred), async patterns, and cloud infrastructure (AWS, GCP, or Azure).
• Experience with observability tooling: structured logging, distributed tracing, and building dashboards for AI system health.

Preferred Qualifications

• Experience in fintech, financial services, or any regulated industry where AI reliability and auditability are critical.
• Familiarity with prompt evaluation frameworks, A/B testing AI outputs, and tracking model performance degradation in production.
• Experience with ML lifecycle management tools: MLflow, Weights & Biases, Vertex AI, or SageMaker.
• Knowledge of real-time data streaming (Kafka, Kinesis) for event-driven AI pipelines.
• Contributions to open-source AI tooling, published technical writing, or talks at AI/ML conferences.
• Prior startup or scale-up experience — comfortable with ambiguity and building foundational systems from scratch.