Sr. AI Engineer

Centric Software•Campbell, CA

12h•$180,000 - $210,000

About The Position

About Centric Software: Centric Software® is a global leader, providing an innovative and AI-enabled product-concept-to-commercialization platform for retailers, brands and manufacturers of all sizes. We equip retail, fashion, luxury, footwear, outdoor, home and consumer goods brands with pioneering best-of-breed solutions to plan, design, develop, source, comply, buy, make, price, allocate, sell and replenish products. Our technology powers brands to streamline processes, drive efficiency and operate with confidence in an ever-changing market. Our story is one of rapid growth, bold ideas and extraordinary opportunities. We’re here to challenge the status quo—and we’re looking for brilliant people who want to do the same. No matter where you are in the world, this is your chance to be part of something exceptional. Senior AI Engineer - AWS SaaS | Production RAG | AI Agents | AI-Augmented Engineering Location: US, Canada or India Compensation: $180,000 - $210,000 USD (based on location) Job Summary We are seeking a Senior AI Engineer to design, build, and operate production-grade AI systems within our enterprise, multi-tenant SaaS platform. This is a hands-on engineering role for a senior SaaS architect who has evolved into an applied AI systems builder. You will architect and ship reliable, scalable AI capabilities used by enterprise customers in production environments. This is not a research role. This is not a prototype-only position. This is a production AI systems engineering role embedded in a mature SaaS platform.

Requirements

Core SaaS Engineering
10+ years of professional software engineering experience
Proven experience building and operating AWS-based SaaS platforms
Strong background in: Serverless architectures Microservices Multi-tenant enterprise systems
Backend expertise in Python
Full-stack capability with React + TypeScript
Deep experience with PostgreSQL, including: Performance tuning Indexing strategies Full-text search
Applied AI Engineering (Production Systems)
2–4+ years building production AI systems
Hands-on experience delivering multiple production RAG systems
Experience implementing hybrid search systems combining: BM25 (or equivalent lexical ranking) Semantic retrieval using embeddings Reranking techniques
Experience building AI agents with structured tool invocation
Experience designing evaluation frameworks and measurable AI quality systems
Strong understanding of: Hallucination mitigation strategies Prompt injection defense Safe tool execution Deterministic workflow design
Technical Expertise We expect hands-on production experience with many of the following (or strong equivalents): RAG & Retrieval Frameworks LlamaIndex, LangChain, or similar frameworks Search & Retrieval Infrastructure PostgreSQL + pgvector (or equivalent vector database) BM25 or similar lexical ranking Hybrid retrieval architecture Agent Frameworks Structured tool-calling frameworks (e.g., Google ADK, AWS Strands, or similar) Orchestration (Required) AWS Step Functions AWS Lambda Deterministic state machine design Evaluation & Observability RAG evaluation frameworks (e.g., RAGAS, DeepEval, or similar) LLM-as-judge evaluation approaches AI system monitoring and observability (e.g., OpenTelemetry) Monitoring for latency, cost, and quality drift AI-Augmented Software Development (Core Competency) This role requires demonstrated professional use of AI-assisted engineering tools. You should have hands-on experience using tools such as: OpenAI Codex or Codex-based environments Claude Code GitHub Copilot-class systems Equivalent AI-powered development platforms You should be able to demonstrate how you: Use AI code generation to accelerate backend and frontend development Generate tests and refactor production code using AI tools Scaffold services with AI assistance Review and productionize AI-generated code responsibly Integrate AI-assisted workflows into CI/CD pipelines Maintain high engineering standards while leveraging AI acceleration AI-augmented engineering is a required capability for this role.
What Strong Candidates Can Clearly Explain A production RAG system they built end-to-end How they improved hybrid retrieval relevance An AI agent workflow and how tool execution was controlled Their approach to measuring AI system quality How they used AWS Step Functions to orchestrate AI workflows How they leverage AI tools to improve engineering productivity without sacrificing quality

Responsibilities

Architect & Deliver Production AI Systems
Design and deploy Retrieval-Augmented Generation (RAG) systems integrated with enterprise data
Build hybrid search pipelines combining lexical (BM25) and semantic retrieval
Implement cross-encoder reranking to improve relevance and precision
Develop structured AI agents with controlled, deterministic tool execution
Continuously improve retrieval quality, task success rates, and system reliability
Build AWS-Native AI Services
Develop AI services using AWS Lambda
Orchestrate AI workflows using AWS Step Functions
Design deterministic state machines with robust retry, timeout, and idempotency strategies
Ensure systems are scalable, cost-efficient, observable, and production-ready
Own AI Quality & Reliability
Design and maintain evaluation pipelines for AI systems
Establish golden datasets and measurable quality benchmarks
Monitor retrieval performance, latency, cost, and system health
Improve AI reliability through disciplined iteration and measurement
Deliver Full-Stack AI Features
Build backend AI services in Python
Deliver AI-powered user experiences in React + TypeScript
Integrate AI workflows into enterprise-grade SaaS applications
Elevate Engineering Standards
Contribute reusable AI platform components
Mentor engineers in applied AI system design
Promote disciplined, measurable AI engineering practices