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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** Diversity Inc - **Location:** Los Angeles, CA, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Automation of Tests, Cloud Computing, Databases, Continuous Integration, Data Governance, Identity and Access Management, Python (Programming Language), Key Management, Network Control, PCI Data Security Standards, Role-Based Access Control, Zero Trust Network Access, Secure Coding, Software Engineering, SQL Databases, Data Logging, Retrieval-Augmented Generation, Flask (Web Framework), Delivery Pipeline, Large Language Models, Multi-Agent Systems, Software Troubleshooting, Generative AI, Indexer, Amazon Virtual Private Cloud (VPC), Backend, Agentic-AI, Rate Limiting, Fastapi, Information Technology, Low Latency, Enterprise Integration, Graphql, Machine Learning Operations, Front End Software Development, Artificial Intelligence Governance, Restful APIs, Model Context Protocol, Human in the Loop, Microservices, Agent2Agent Protocol - **Published:** October 17, 2026 - **Apply:** https://www.careerbuilder.com/job-details/ai-engineer-los-angeles-ca--2f0adf81-a08b-48f5-93c4-5bbb4a219f6b ## About the Role Experience: 8+ years in software engineering with proven experience with at least 2+ years in hands on GenAI and AI-DLC applications to production. Agentic Frameworks & Protocols: Deep experience with MCP, A2A, multi-agent systems, function/tool calling, VPC, taxonomy and stateful agent orchestration.Advanced AI/ML: Demonstrated ability in fine-tuning, embedding generation, hybrid RAG, model selection trade-offs, and prompt/context engineering. Engineering Rigor: Mastery of automated testing, secure coding (PII/PCI compliance), CI/CD, and performance troubleshooting (latency, throughput, cost).Education: Bachelor's degree in Computer Science, Engineering, Data Science, or equivalent practical experience. Preferred Qualifications Full-Stack & Systems: Strong hands-on proficiency in Python, modern web frontends, microservices, REST/GraphQL APIs, SQL/vector databases, containers, and cloud infrastructure.Education: Masters degree in Computer Science, Engineering, Data Science, or equivalent practical experience. Work Experience 5-7 Years ## Description Primary Skill : Generative AI, fastapi, Flask Hands-on AI Engineer who can design, build, and operationalize enterprise Agentic AI solutions. Rather than calling standard LLM APIs, you will build multi-agent ecosystems, implement cutting-edge protocol integrations (MCP, A2A, VPC), customize task-specific models, and engineer end-to-end full-stack applications with production-grade security, low latency, and cost control. Key Responsibilities 1. Advanced Agent & Multi-Agent EngineeringOrchestration & Protocols: Build autonomous agents and multi-agent workflows using Model Context Protocol (MCP) for tool integration and Agent-to-Agent (A2A) protocols for task delegation.Agent Capabilities: Design agent state management, short/long-term memory, dynamic planning, reasoning loops, and human-in-the-loop (HITL) fallback paths.Model Optimization: Fine-tune, customize, and select models (LLMs vs. small task-specific models) based on empirical cost, latency, and accuracy metrics. 2. Enterprise RAG & GroundingAdvanced Retrieval: Design chunking, indexing, reranking, and hybrid search (vector + keyword) pipelines with citation enforcement.Data Privacy: Enforce tenant isolation and RBAC boundaries to prevent unauthorized data cross-contamination. 3. Automated Evaluation, MLOps & Guardrails Systematic Evaluation: Build automated evaluation suites to benchmark groundedness, task completion, tool choice, safety, and agent behavior across updates. Observability & CI/CD: Establish automated pipelines (Dev/Test/Prod), structured logging, tracing, token-cost tracking, and real-time operational alerting. AI Guardrails: Implement active protections against prompt injection, jailbreaking, unsafe tool execution, and supply-chain vulnerabilities. 4. AI Security, Governance & ComplianceThreat Mitigation: Implement active guardrails against prompt injection, jailbreaking, vector store poisoning, insecure output handling, and supply-chain vulnerabilities.Access & Data Governance: Enforce Zero-Trust architecture, identity and access management (IAM), secrets management, and secure authorization controls across APIs, agents, and tool execution.Control Plane Controls: Develop AI control plane mechanisms, including policy enforcement, telemetry, emergency kill-switches, and automated rollback workflows 5. Automated Evaluation, MLOps & GuardrailsFull-Stack & Enterprise ArchitectureEnd-to-End Application Engineering: Build responsive frontends and backend microservices using REST, GraphQL, and event-driven architectures.Secure Enterprise Integration: Connect agents to enterprise databases, internal APIs, and Machine-to-Cloud (M2C) platforms while enforcing authorization-aware tool execution, rate limiting, and circuit breakers. Required Qualifications Experience: 8+ years in software engineering with proven experience with at least 2+ years in hands on GenAI and AI-DLC applications to production. Agentic Frameworks & Protocols: Deep experience with MCP, A2A, multi-agent systems, function/tool calling, VPC, taxonomy and stateful agent orchestration.Advanced AI/ML: Demonstrated ability in fine-tuning, embedding generation, hybrid RAG, model selection trade-offs, and prompt/context engineering. Engineering Rigor: Mastery of automated testing, secure coding (PII/PCI compliance), CI/CD, and performance troubleshooting (latency, throughput, cost).Education: Bachelor's degree in Computer Science, Engineering, Data Science, or equivalent practical experience. Preferred Qualifications Full-Stack & Systems: Strong hands-on proficiency in Python, modern web frontends, microservices, REST/GraphQL APIs, SQL/vector databases, containers, and cloud infrastructure.Education: Masters degree in Computer Science, Engineering, Data Science, or equivalent practical experience. Work Experience 5-7 Years Primary Skill : Generative AI, fastapi, Flask Hands-on AI Engineer who can design, build, and operationalize enterprise Agentic AI solutions. Rather than calling standard LLM APIs, you will build multi-agent ecosystems, implement cutting-edge protocol integrations (MCP, A2A, VPC), customize task-specific models, and engineer end-to-end full-stack applications with production-grade security, low latency, and cost control. Key Responsibilities 1. Advanced Agent & Multi-Agent EngineeringOrchestration & Protocols: Build autonomous agents and multi-agent workflows using Model Context Protocol (MCP) for tool integration and Agent-to-Agent (A2A) protocols for task delegation.Agent Capabilities: Design agent state management, short/long-term memory, dynamic planning, reasoning loops, and human-in-the-loop (HITL) fallback paths.Model Optimization: Fine-tune, customize, and select models (LLMs vs. small task-specific models) based on empirical cost, latency, and accuracy metrics. 2. Enterprise RAG & GroundingAdvanced Retrieval: Design chunking, indexing, reranking, and hybrid search (vector + keyword) pipelines with citation enforcement.Data Privacy: Enforce tenant isolation and RBAC boundaries to prevent unauthorized data cross-contamination. 3. Automated Evaluation, MLOps & Guardrails Systematic Evaluation: Build automated evaluation suites to benchmark groundedness, task completion, tool choice, safety, and agent behavior across updates. Observability & CI/CD: Establish automated pipelines (Dev/Test/Prod), structured logging, tracing, token-cost tracking, and real-time operational alerting. AI Guardrails: Implement active protections against prompt injection, jailbreaking, unsafe tool execution, and supply-chain vulnerabilities. 4. AI Security, Governance & ComplianceThreat Mitigation: Implement active guardrails against prompt injection, jailbreaking, vector store poisoning, insecure output handling, and supply-chain vulnerabilities.Access & Data Governance: Enforce Zero-Trust architecture, identity and access management (IAM), secrets management, and secure authorization controls across APIs, agents, and tool execution.Control Plane Controls: Develop AI control plane mechanisms, including policy enforcement, telemetry, emergency kill-switches, and automated rollback workflows 5. Automated Evaluation, MLOps & GuardrailsFull-Stack & Enterprise ArchitectureEnd-to-End Application Engineering: Build responsive frontends and backend microservices using REST, GraphQL, and event-driven architectures.Secure Enterprise Integration: Connect agents to enterprise databases, internal APIs, and Machine-to-Cloud (M2C) platforms while enforcing authorization-aware tool execution, rate limiting, and circuit breakers. Required Qualifications Experience: 8+ years in software engineering with proven experience with at least 2+ years in hands on GenAI and AI-DLC applications to production. Agentic Frameworks & Protocols: Deep experience with MCP, A2A, multi-agent systems, function/tool calling, VPC, taxonomy and stateful agent orchestration.Advanced AI/ML: Demonstrated ability in fine-tuning, embedding generation, hybrid RAG, model selection trade-offs, and prompt/context engineering. Engineering Rigor: Mastery of automated testing, secure coding (PII/PCI compliance), CI/CD, and performance troubleshooting (latency, throughput, cost).Education: Bachelor's degree in Computer Science, Engineering, Data Science, or equivalent practical experience. Preferred Qualifications Full-Stack & Systems: Strong hands-on proficiency in Python, modern web frontends, microservices, REST/GraphQL APIs, SQL/vector databases, containers, and cloud infrastructure.Education: Masters degree in Computer Science, Engineering, Data Science, or equivalent practical experience. Work Experience 5-7 Years