Database Engineering-Lead Engineer

The Opportunity Join FICO’s platform engineering team to architect and operate the AI/ML infrastructure powering FICO Assistant — our client-facing AI product serving financial institutions across APAC, EMEA, and North America. You’ll own production AI/ML systems end-to-end: model serving, training pipelines, vector search, cloud databases, and 24x7 operational support. What You’ll Do 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) Amazon S3 — Langfuse storage and model artifact management Implement AI observability using Langfuse — latency tracking, token economics, hallucination detection, and response quality metrics Build Infrastructure as Code (Terraform, Crossplane, CloudFormation) for all AI/ML and database resources Own 24x7 production support for FICO Assistant — proactive monitoring, incident management, and SLA compliance Implement CI/CD pipelines for ML systems with automated testing and model validation gates Design and enforce database security — IAM authentication, TLS, encryption at rest/in-transit, and fine-grained access controls Implement monitoring and alerting using CloudWatch and Grafana across all database and AI/ML services Collaborate with data scientists, ML engineers, and product teams to deliver scalable infrastructure What We’re Looking For Must Have: 8+ years infrastructure/platform engineering; 3+ years focused on AI/ML infrastructure Hands-on ML model serving — SageMaker, Bedrock, vLLM, or TGI AWS managed database operations — provisioning, scaling, backup/DR, performance tuning across multiple database engines NoSQL/document database design — DocumentDB/MongoDB schema design, compound indexing, TTL policies, change streams PostgreSQL administration — Aurora PostgreSQL performance tuning, connection pooling, query optimization In-memory cache architecture — Redis cluster management, eviction policies, memory monitoring Vector search / embedding index management — OpenSearch k-NN, dimension tuning, index optimization for RAG MLOps tooling: experiment tracking (MLflow, W&B), model registries, CI/CD for ML Infrastructure as Code: Terraform, Crossplane, or CloudFormation for database and AI/ML resources 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 Comfortable with “you build it, you run it” ownership and on-call rotation Nice to Have: Foundation model fine-tuning (LoRA, QLoRA, RLHF) AI agent frameworks and autonomous system orchestration Graph databases (Neo4j, Neptune) for knowledge graphs AWS ML Specialty or Database Specialty certification Working Arrangements Hours: 2-11PM IST MON-FRI On-Call: Rotating alternate weekends Support Model: 24x7 follow-the-sun for client-facing AI product Tech Stack Area Technologies: AI/ML SageMaker, Bedrock, vLLM, MLflow, LangChain, Ray, Airflow Databases Aurora PostgreSQL, DocumentDB, OpenSearch, ElastiCache Redis, S3 Observability Langfuse, LangSmith, CloudWatch, Grafana, Prometheus Platform Kubernetes (EKS), Docker, Helm, ArgoCD, Terraform, Crossplane Languages Python, Go, Bash Why FICO Build the infrastructure behind a live, client-facing AI product at enterprise scale Work with cutting-edge ML/AI technologies in production (not just POCs) Global impact — powering fraud detection and credit decisioning for major financial institutions Competitive compensation, flexible work, and comprehensive benefits