AI Engineer - Architect level (only 13 to 15 years)

Kaizen Technologies; Los Angeles (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Kaizen Technologies is seeking an experienced AI Engineer at architect level to design, build, and operationalize enterprise Agentic AI solutions. You will create multi-agent ecosystems, implement cutting-edge protocols (MCP, A2A), customize task-specific models, and develop production-grade full-stack apps with security, low latency, and cost controls.

You will lead advanced agent engineering, data privacy, MLOps guardrails, and secure integrations with enterprise databases and APIs, ensuring

Qualifications

• 12+ years in software engineering with 2+ years in GenAI/AI-DLC in production.
• Deep experience with MCP, A2A, multi-agent systems and tool calling.
• Strong ability to fine-tune models, embeddings, and RAG approaches.
• Solid CI/CD, automated testing, secure coding and latency optimization.

Responsibilities

• Design autonomous agents and multi-agent workflows using MCP and A2A.
• Define agent memory, state management, planning and HITL fallbacks.
• Fine-tune and select models based on cost, latency, and accuracy.
• Build secure, low-latency production-grade full-stack apps.
• Enforce data privacy, RBAC, and tenant isolation across tools.

Skills

Generative AI

FastAPI

Flask

Multi-agent

MCP

A2A

Memory management

Education

Bachelor's degree

Tools

VPC

RBAC

CI/CD tooling

Job description

Title: AI Engineer - Architect level - Need only 13 to 15 years

Engineers with not more than 15 years of exp

Primary Skill : Generative AI, fastapi, Flask

Role Summary

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 Engineering

• Orchestration & 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.
• Advanced 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 & Compliance

• Threat 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 & Guardrails

• End-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: 12+ 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.