AI Engineer

As an AI Engineer, you will design and build next-generation AI applications that leverage agentic workflows, large language models, and enterprise knowledge systems. You will work across the full software lifecycle, from architecture and backend development to deployment, evaluation, monitoring, and optimization.

The ideal candidate is excited about the potential of AI while maintaining strong engineering judgment, carefully balancing reliability, security, performance, operational complexity, cost, and business value.

Core Technical Areas

Agent Architectures & Multi-Agent Systems

Agent orchestration frameworks such as Google ADK, LangChain, LangGraph, DeepAgents, AutoGen, CrewAI, or similarPlanning and reasoning workflowsTool integration and function callingState and memory managementHuman-in-the-loop review processesError handling and recovery mechanisms

LLMs & Generative AI

Transformer fundamentals and attention mechanismsPrompt engineering and structured outputsContext management and token optimizationFine-tuning and model customizationWorking with both commercial and open-source models

RAG & Knowledge Systems

Semantic and hybrid searchDocument ingestion and processing pipelinesEmbeddings and retrieval strategiesReranking techniquesVector databases such as Pinecone, Qdrant, Weaviate, pgvector, or Vertex AI Vector SearchKnowledge access controls and source citation capabilities

Backend & Cloud Engineering

Python, FastAPI, and asynchronous programmingREST and streaming APIsEvent-driven and microservice architecturesDocker and cloud deploymentSecurity, monitoring, and observability

AI-Augmented Development

Development acceleration using tools such as Cursor, GitHub Copilot, Claude Code, or similarFrontend development using React, Next.js, and TypeScript

Evaluation & Reliability

Agent evaluation and testingObservability and tracingPrompt security and guardrailsLatency, quality, and cost monitoringTools such as LangSmith, Phoenix, OpenTelemetry, and cloud-native monitoring platformsDesign, build, and maintain agentic and multi-agent AI systems for complex business workflowsDevelop scalable backend services and APIs that support AI-powered applicationsBuild and maintain RAG pipelines, retrieval systems, and enterprise knowledge platformsImplement evaluation frameworks to measure quality, reliability, safety, latency, and costEstablish monitoring, tracing, security, and governance practices for production AI servicesCollaborate with product, engineering, and business stakeholders to deliver impactful AI solutionsEvaluate emerging AI models, frameworks, and tools to improve product quality and development efficiencyContribute to architecture decisions and engineering best practices

Required Qualifications

4+ years of software engineering and/or AI engineering experienceProven experience delivering production-grade software and AI applicationsHands-on experience building agentic applications involving multi-step workflows, tool usage, orchestration, memory, and fault handlingStrong proficiency in Python and FastAPIExperience with asynchronous programming, API development, and containerized applicationsExperience with LLM-based applications, RAG architectures, vector databases, and model evaluationFamiliarity with agent frameworks such as Google ADK, LangChain, LangGraph, DeepAgents, AutoGen, CrewAI, or similarExperience deploying secure, scalable applications in cloud environmentsStrong problem-solving, communication, and collaboration skills

Preferred Qualifications

Experience with Google Cloud Platform, Vertex AI, Gemini, and Google's agent ecosystemExperience with observability platforms such as LangSmith, Phoenix, or OpenTelemetryExperience building full-stack AI applications using React, Next.js, and TypeScriptKnowledge of CI/CD, infrastructure automation, and modern DevOps practicesExperience working in enterprise-scale AI environments