Lead Database Platform Engineer

Optimization Lead Database Platform Engineer AI/ML Infrastructure

This is a software engineering and platform engineering role focused on building scalable data infrastructure, distributed systems, cloud-native data platforms, and developer tooling. This is not a database administration (DBA) role.

The Opportunit

yJoin FICO's platform engineering team as a Database Platform Engineer a role at the intersection of databases, data platforms, and AI/ML infrastructure. You will architect and operate the data platform capabilities powering FICO Assistant our client-facing AI product serving financial institutions across APAC, EMEA, and North America. Your job is not to administer databases, but to build the platform capabilities that application developers, data engineers, data scientists, and ML engineers use. You'll own production AI/ML data systems end-to-end: model serving infrastructure, training pipelines, vector search, cloud database platforms, and 24x7 operational support

.
What You'll

Do1. Build Database Platform Capabiliti

esDefine and build platform features that enable scalable, resilient data infrastructur

• e:Design and build database platform capabilities query optimization, data storage engines, replication and consistency mechanisms, backup and recovery syste
• msOwn multi-tenancy and scalability for the FICO Assistant database layer acros
• s:Amazon OpenSearch embeddings and vector search (k-NN indexes for RA
• G)Amazon DocumentDB conversation/session storage (MongoDB-compatible schema design, compound/TTL indexing, change stream
• s)Aurora PostgreSQL Langfuse observability backe
• ndAmazon ElastiCache Redis Langfuse caching layer (shard management, eviction policies, Multi-AZ failove
• r)Amazon S3 Langfuse storage and model artifact manageme
• ntBuild Infrastructure as Code (Terraform, Crossplane, CloudFormation) for all database and AI/ML resourc
• esImplement performance monitoring and observability CloudWatch, Grafana, and custom database metri
• csDesign and enforce database security IAM authentication, TLS, encryption at rest/in-transit, and fine-grained access contro

ls2. Enable AI/ML Workloads on Data Platfor

msModern databases increasingly support AI use cases. Build the capabilities that make this possibl

• e:Design and operate production AI/ML infrastructure model serving (SageMaker, Bedrock, self-hosted LLMs on EKS), training pipelines, and inference optimizati
• onBuild vector search and embedding index management OpenSearch k-NN, dimension tuning, index optimization for Retrieval-Augmented Generation (RA
• G)Implement in-database ML capabilities and integrate vector databases with AI syste
• msImplement AI observability using Langfuse latency tracking, token economics, hallucination detection, and response quality metri
• csSupport generative AI applications with feature stores, similarity search, and embedding stora
• geImplement CI/CD pipelines for ML systems with automated testing and model validation gat

es3. Build Data-to-AI Pipelin

esCreate integrations between data platforms and ML/LLM system

• s:Build data-to-ML workflows connecting data warehouses, data lakes, ML platforms, and LLM platfor
• msCreate data pipelines that feed training data, embeddings, and feature stores into AI syste
• msCollaborate with data scientists, ML engineers, and product teams to deliver scalable infrastructu
• reOwn 24x7 production support for FICO Assistant proactive monitoring, incident management, and SLA complian

ce
What We're Looking

ForMust Ha

• ve:8+ years infrastructure/platform engineering; 3+ years focused on AI/ML infrastructure or data platfo
• rmsHands-on ML model serving SageMaker, Bedrock, vLLM, or
• TGIAWS managed database operations provisioning, scaling, backup/DR, performance tuning across multiple database engi
• nesNoSQL/document database design DocumentDB/MongoDB schema design, compound indexing, TTL policies, change stre
• amsInfrastructure as Code: Terraform, Crossplane, or CloudFormation for database and AI/ML resour
• cesKubernetes (EKS) for ML workloads GPU node pools, autoscaling, service m
• eshDatabase security: IAM auth, TLS, encryption, fine-grained access contr
• olsComfortable with "you build it, you run it" ownership and on-call rotat

ion
Nice to H

• ave:Foundation model fine-tuning (LoRA, QLoRA, R
• LHF)PostgreSQL administration Aurora PostgreSQL performance tuning, connection pooling, query optimiza
• tionIn-memory cache architecture Redis cluster management, eviction policies, memory monito
• ringVector search / embedding index management OpenSearch k-NN, dimension tuning, index optimization for
• RAGMLOps tooling: experiment tracking (MLflow, W&B), model registries, CI/CD fo
• r MLAI agent frameworks and autonomous system orchestra
• tionGraph databases (Neo4j, Neptune) for knowledge gr
• aphsAWS ML Specialty or Database Specialty certifica
• tionExperience at companies building data platforms (Snowflake, Databricks, AWS, Google, Micros

oft)
Working Arrange

• mentsHours: 2 PM 11 PM India Time Zone (week
• days)On-Call: Rotating alternate wee
• kendsSupport Model: 24x7 follow-the-sun for client-facing AI pr

oduct
Tech

• StackAI/ML: SageMaker, Bedrock, EKS (GPU), Langfuse, Lan
• gChainDatabases: OpenSearch, DocumentDB, Aurora PostgreSQL, ElastiCache Red
• is, S3IaC: Terraform, Crossplane, CloudFor
• mationMonitoring: CloudWatch, Grafana, Prom
• etheusCI/CD: GitHub Actions, ArgoCD,
• MLflowProgramming language: Go and

python
W

• hy FICOBuild the data platform behind a live, client-facing AI product at enterpris
• e scaleWork with cutting-edge ML/AI technologies in production (not jus
• t POCs)Global impact powering fraud detection and credit decisioning for major financial insti
• tutionsCompetitive compensation, flexible work, and comprehensive b