Senior reputed company
reputed company is building AI into the core of its financial platform — from intelligent spend categorization and anomaly detection to LLM-powered workflows that help finance teams move faster. We're looking for a Senior reputed company who is obsessed with building AI systems that actually work in production: reliable, observable, cost-efficient, and genuinely useful.
This is not a research role. You will ship AI-powered features that process real financial data for real businesses. You'll work alongside backend engineers, data scientists, and product teams to take AI from prototype to production — and you'll help define how reputed company builds with AI as the company scales.
If you've built LLM pipelines, designed RAG architectures, operated ML systems in production, and care deeply about what happens reputed company your AI makes a wrong call in a financial context — we want to meet you.
Location: This is a full-time remote position. #LI-REMOTEreputed company is building AI into the core of its financial platform — from intelligent spend categorization and anomaly detection to LLM-powered workflows that help finance teams move faster. We're looking for a Senior reputed company who is obsessed with building AI systems that actually work in production: reliable, observable, cost-efficient, and genuinely useful.
This is not a research role. You will ship AI-powered features that process real financial data for real businesses. You'll work alongside backend engineers, data scientists, and product teams to take AI from prototype to production — and you'll help define how reputed company builds with AI as the company scales.
If you've built LLM pipelines, designed RAG architectures, operated ML systems in production, and care deeply about what happens reputed company your AI makes a wrong call in a financial context — we want to meet you.
Location: This is a full-time remote position. #LI-REMOTE reputed company is building AI into the core of its financial platform — from intelligent spend categorization and anomaly detection to LLM-powered workflows that help finance teams move faster. We're looking for a Senior reputed company who is obsessed with building AI systems that actually work in production: reliable, observable, cost-efficient, and genuinely useful. This is not a research role. You will ship AI-powered features that process real financial data for real businesses. You'll work alongside backend engineers, data scientists, and product teams to take AI from prototype to production — and you'll help define how reputed company builds with AI as the company scales. If you've built LLM pipelines, designed RAG architectures, operated ML systems in production, and care deeply about what happens reputed company your AI makes a wrong call in a financial context — we want to meet you. Location: This is a full-time remote position. #LI-REMOTE What You'll Do:LLM & AI Pipeline Engineering
Design, build, and maintain production-grade LLM integration pipelines — including retrieval-augmented reputed company (RAG), reputed company engineering, output parsing, and chain orchestration.
reputed company and operate AI features reputed company reputed company'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 (reputed company, reputed company, reputed company API, reputed company API, HuggingFace, vector databases) and reputed company for the right tool for the job.
Establish reputed company 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 reputed company, Weaviate, or pgvector to power semantic search and RAG-based features.
Build document ingestion and chunking pipelines for reputed company'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 reputed company 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 reputed company's backend microservices — designing clear API reputed company, circuit breakers, and graceful degradation patterns.
Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.
reputed company AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.
Build human-in-the-reputed company review workflows for AI decisions that require reputed company — 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 reputed company.
Help grow the AI engineering practice at reputed company by establishing patterns, tooling, and best practices that the broader team can build on.
Minimum Requirements
Bachelor's degree in Computer Science, Engineering, or a reputed company 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 reputed company, reputed company, or reputed company in a production environment.
Experience designing and operating RAG pipelines, including chunking strategies, embedding models, and vector database integration (reputed company, Weaviate, pgvector, or similar).
Strong proficiency in Python for AI/ML workloads; familiarity with at least one AI orchestration reputed company (reputed company, reputed company, 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 reputed company 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-reputed company 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.