Sr. AI Engineer

Centric Software is a global leader in providing innovative and AI-enabled product-concept-to-commercialization platforms for various brands. They are seeking a Senior AI Engineer to design, build, and operate production-grade AI systems within their enterprise SaaS platform, focusing on scalable and reliable AI capabilities for enterprise customers.

Responsibilities:

• 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
• 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
• 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
• Build backend AI services in Python
• Deliver AI-powered user experiences in React + TypeScript
• Integrate AI workflows into enterprise-grade SaaS applications
• Contribute reusable AI platform components
• Mentor engineers in applied AI system design
• Promote disciplined, measurable AI engineering practices

Requirements:

• 10+ years of professional software engineering experience
• Proven experience building and operating AWS-based SaaS platforms
• Strong background in serverless architectures
• Strong background in microservices
• Strong background in multi-tenant enterprise systems
• Backend expertise in Python
• Full-stack capability with React + TypeScript
• Deep experience with PostgreSQL, including performance tuning, indexing strategies, and 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) and semantic retrieval using embeddings and 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
• Strong understanding of prompt injection defense
• Strong understanding of safe tool execution
• Strong understanding of deterministic workflow design
• Hands-on production experience with RAG & Retrieval Frameworks such as LlamaIndex, LangChain, or similar frameworks
• Hands-on production experience with search & retrieval infrastructure including PostgreSQL + pgvector (or equivalent vector database), BM25 or similar lexical ranking, and hybrid retrieval architecture
• Hands-on production experience with agent frameworks including structured tool-calling frameworks (e.g., Google ADK, AWS Strands, or similar)
• Hands-on production experience with orchestration tools including AWS Step Functions and AWS Lambda
• Hands-on production experience with deterministic state machine design
• Hands-on production experience with evaluation & observability tools including RAG evaluation frameworks (e.g., RAGAS, DeepEval, or similar), LLM-as-judge evaluation approaches, AI system monitoring and observability (e.g., OpenTelemetry), and monitoring for latency, cost, and quality drift
• Demonstrated professional use of AI-assisted engineering tools such as OpenAI Codex or Codex-based environments, Claude Code, GitHub Copilot-class systems, or equivalent AI-powered development platforms
• Ability to demonstrate how to use AI code generation to accelerate backend and frontend development
• Ability to generate tests and refactor production code using AI tools
• Ability to scaffold services with AI assistance
• Ability to review and productionize AI-generated code responsibly
• Ability to integrate AI-assisted workflows into CI/CD pipelines
• Ability to maintain high engineering standards while leveraging AI acceleration