K
AI Engineer - Architect level (only 13 to 15 years)
Kaizen Technologies
2 hours ago
Full-time
On-site
Los Angeles, California, United States
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.
#J-18808-Ljbffr
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.
#J-18808-Ljbffr