Sr. AI Engineer

Centric Software•Campbell, CA

1d•Remote

About The Position

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

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
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
Hands-on production experience with many of the following (or strong equivalents): RAG & Retrieval Frameworks (LlamaIndex, LangChain, or similar), 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 (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)
Demonstrated professional use of AI-assisted engineering tools (e.g., OpenAI Codex, Claude Code, GitHub Copilot-class systems, Equivalent AI-powered development platforms)
Ability to demonstrate how to: 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.
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

Nice To Haves

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
What This Role Is Not
Not a pure machine learning research role
Not a fine-tuning-only role
Not a prompt engineering-only role
Not a chatbot demo builder role
The Ideal Candidate
You are a senior SaaS engineer who:
Designs systems for reliability and scale
Ships production AI capabilities
Measures quality, not just functionality
Understands cost and performance tradeoffs
Uses AI to build better software—faster—without lowering standards

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

Benefits

Centric Software provides equal employment opportunities to all qualified applicants without regard to race, sex, sexual orientation, gender identity, national origin, color, age, religion, protected veteran or disability status or genetic information.

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