Senior AI Engineer

This is a fully remote position, open to applicants in Argentina.

📋 Description

• Design, construct, and maintain robust production-grade LLM integration pipelines, encompassing retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.

• Develop and manage AI functionalities within Jeeves's core financial products, including spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.

• Implement structured output validation, fallback mechanisms, and confidence scoring to ensure AI-driven decisions adhere to reliability standards for financial applications.

• Assess and incorporate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) while advocating for the most suitable tools for specific tasks.

• Establish practices for prompt versioning and evaluation to maintain the accuracy and consistency of AI outputs as models and data evolve.

• Design and oversee vector search pipelines utilizing databases like Pinecone, Weaviate, or pgvector to enhance semantic search and RAG-based functionalities.

• Create document ingestion and chunking pipelines for Jeeves's financial data, processing invoices, receipts, policy documents, and transaction records.

• Enhance retrieval quality through the selection of embedding models, chunk strategies, metadata filtering, and re-ranking techniques.

• Collaborate with data scientists to transition trained ML models from experimental notebooks to production-serving infrastructures.

• Develop and uphold model serving endpoints with suitable latency SLOs, input validation, and output monitoring.

• Implement monitoring for model performance and data drift detection to ensure ongoing accuracy of production models.

• Support workflows for model retraining by designing clean data pipelines and feature engineering that can be continuously updated.

• Seamlessly integrate AI services with Jeeves's backend microservices by designing clear API contracts, circuit breakers, and patterns for graceful degradation.

• Write high-quality, testable backend code in Python or Go/Node.js to support AI-integrated functionalities.

• Equip AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.

• Create human-in-the-loop review workflows for AI decisions that necessitate oversight, especially for high-value financial actions.

• Collaborate with Product, Backend Engineering, and Data Science teams to define the AI roadmap and translate requirements into dependable systems.

• Cultivate a culture of quality by composing design documentation, reviewing peers' AI system designs, and sharing insights openly.

• Contribute to the growth of the AI engineering practice at Jeeves by establishing patterns, tools, and best practices for the broader team to build upon.

⛳️ Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

• 5+ years of professional software engineering experience, with a minimum of 3 years dedicated to AI/ML systems in production.

• Hands-on experience in building and deploying LLM-powered applications utilizing APIs such as OpenAI, Anthropic, or Cohere within a production context.

• Familiarity with 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 and familiarity with at least one AI orchestration framework (LangChain, LlamaIndex, or equivalent).

• Experience with ML model serving infrastructure, including REST or gRPC inference endpoints, input/output validation, latency budgeting, and monitoring.

• Solid understanding of backend engineering principles, including REST APIs, relational databases (PostgreSQL preferred), async patterns, and cloud infrastructure (AWS, GCP, or Azure).

• Proficiency with observability tools such as structured logging, distributed tracing, and creating dashboards for AI system health monitoring.

• Experience in the fintech sector, financial services, or any regulated industry where AI reliability and auditability are essential.

• Familiarity with prompt evaluation frameworks, A/B testing of AI outputs, and monitoring model performance degradation in live production.

• Knowledge of ML lifecycle management tools such as MLflow, Weights & Biases, Vertex AI, or SageMaker.

• Understanding of real-time data streaming technologies (Kafka, Kinesis) for event-driven AI pipelines.

• Contributions to open-source AI tools, technical writing publications, or presentations at AI/ML conferences.

• Previous experience in a startup or scale-up environment, demonstrating comfort with ambiguity and the ability to build foundational systems from the ground up.

🏝️ Benefits

• Competitive salary and comprehensive benefits package.

• Opportunities for professional growth and development.

• Flexible working arrangements to support work-life balance.

• A collaborative and innovative work environment.