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Cloud 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 Opportunity Join 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 Do 1. Build Database Platform Capabilities Define and build platform features that enable scalable, resilient data infrastructure: Design and build database platform capabilities query optimization, data storage engines, replication and consistency mechanisms, backup and recovery systems Build Infrastructure as Code (Terraform, Crossplane, CloudFormation) for all database and AI/ML resources Implement performance monitoring and observability CloudWatch, Grafana, and custom database metrics Design and enforce database security IAM authentication, TLS, encryption at rest/in-transit, and fine-grained access controls 2. Enable AI/ML Workloads on Data Platforms Modern databases increasingly support AI use cases. Build the capabilities that make this possible: Design and operate production AI/ML infrastructure model serving (SageMaker, Bedrock, self-hosted LLMs on EKS), training pipelines, and inference optimization Build vector search and embedding index management OpenSearch k-NN, dimension tuning, index optimization for Retrieval-Augmented Generation (RAG) Implement AI observability using Langfuse latency tracking, token economics, hallucination detection, and response quality metrics Support generative AI applications with feature stores, similarity search, and embedding storage Implement CI/CD pipelines for ML systems with automated testing and model validation gates 3. Build Data-to-AI Pipelines Create integrations between data platforms and ML/LLM systems: Build data-to-ML workflows connecting data warehouses, data lakes, ML platforms, and LLM platforms Create data pipelines that feed training data, embeddings, and feature stores into AI systems Collaborate with data scientists, ML engineers, and product teams to deliver scalable infrastructure Own 24x7 production support for FICO Assistant proactive monitoring, incident management, and SLA compliance What We're Looking For Must Have: 8+ years infrastructure/platform engineering; 3+ years focused on AI/ML infrastructure or data platforms Hands-on ML model serving SageMaker, Bedrock, vLLM, or TGI Infrastructure as Code: Terraform, Crossplane, or CloudFormation for database and AI/ML resources 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 Experience at companies building data platforms (Snowflake, Databricks, AWS, Google, Microsoft) In-memory cache architecture Redis cluster management, eviction policies, memory monitoring PostgreSQL administration Aurora PostgreSQL performance tuning, connection pooling, query optimization 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 Kubernetes (EKS) for ML workloads GPU node pools, autoscaling, service mesh LLM application patterns: conversation memory, guardrails, agent frameworks (LangChain, LlamaIndex) Working Arrangements Hours: 2 PM 11 PM India Time Zone (weekdays) On-Call: Rotating alternate weekends Support Model: 24x7 follow-the-sun for client-facing AI product Tech Stack AI/ML: SageMaker, Bedrock, EKS (GPU), Langfuse, LangChain IaC: Terraform, Crossplane, CloudFormation Monitoring: CloudWatch, Grafana, Prometheus CI/CD: GitHub Actions, ArgoCD, MLflow Programming language: Go and python Why FICO Build the data platform 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 Join a newer category of engineers Database Platform Engineers recognized at AWS, Snowflake, Databricks, Google, and Microsoft Competitive compensation .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.