Lead Engineer, Database Engineering

🕒 4 days ago

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FICO

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1956

💸 Finance

🤖 Artificial Intelligence

☁️ SaaS

Finance • Artificial Intelligence • SaaS

FICO is a leading analytics and software company renowned for its FICO® Score, a tool widely used by lenders to assess credit risk. The company offers a comprehensive platform that leverages data, AI, and machine learning to power intelligent decision-making and customer engagement across various industries. FICO's solutions span fraud detection, credit scoring, and customer lifecycle management, making it vital to sectors such as finance and telecommunications. Its innovative products help businesses optimize outcomes through real-time analytics, business composability, and scenario management.

📋 Description

• Design and operate production AI/ML infrastructure — model serving (SageMaker, Bedrock, self-hosted LLMs on EKS), training pipelines, and inference optimization. • Own the FICO Assistant database layer — provisioning, scaling, performance tuning, and DR across: Amazon OpenSearch — embeddings and vector search (k-NN indexes for RAG). • Amazon DocumentDB — conversation/session storage (MongoDB-compatible schema design, compound/TTL indexing, change streams). • Aurora PostgreSQL — Langfuse observability backend. • Amazon ElastiCache Redis — Langfuse caching layer (shard management, eviction policies, Multi-AZ failover). • Build Infrastructure as Code (Terraform, Crossplane, CloudFormation) for all AI/ML and database resources. • Implement CI/CD pipelines for ML systems with automated testing and model validation gates. • Implement monitoring and alerting using CloudWatch and Grafana across all database and AI/ML services.

🎯 Requirements

• 8+ years infrastructure/platform engineering; 3+ years focused on AI/ML infrastructure. • Hands-on ML model serving — SageMaker, Bedrock, vLLM, or TGI. • Vector search / embedding index management — OpenSearch k-NN, dimension tuning, index optimization for RAG. • Kubernetes (EKS) for ML workloads — GPU node pools, autoscaling, service mesh LLM application patterns: conversation memory, guardrails, agent frameworks (LangChain, LlamaIndex). • Database security: IAM auth, TLS, encryption, fine-grained access controls.

🏖️ Benefits

• An inclusive culture strongly reflecting our core values: Act Like an Owner, Delight Our Customers and Earn the Respect of Others. • The opportunity to make an impact and develop professionally by leveraging your unique strengths and participating in valuable learning experiences. • Highly competitive compensation, benefits and rewards programs that encourage you to bring your best every day and be recognized for doing so. • An engaging, people-first work environment offering work/life balance, employee resource groups, and social events to promote interaction and camaraderie.