What are the responsibilities and job description for the Artificial Intelligence Engineer position at ITMC Systems, Inc?
Job Title: AI Architect/Lead
Location: Paramus, NJ (Hybrid)
Job Type: Fulltime/Contract Position
Job Description:
AI Architect – Google AI & Generative Intelligence
Experience Required: 12–18 Years in Software Engineering | 7 Years in AI/ML & Generative AI
Role Overview
We are seeking a highly accomplished AI Architect with deep expertise in Google AI technologies and Generative AI to lead the design and implementation of enterprise-scale AI solutions. This role requires strong architectural vision, hands-on technical depth, and leadership in building production-grade AI systems leveraging LLMs, SLMs, and multi-agent frameworks.
The ideal candidate will drive AI strategy, define scalable architectures, and lead cross-functional teams in delivering cutting-edge AI-powered applications using the Google Cloud ecosystem, modern AI frameworks, and robust MLOps practices.
Key Responsibilities
1. AI Architecture & Strategy
- Define end-to-end AI/GenAI architecture for enterprise-grade applications.
- Establish best practices for LLM/SLM adoption, multi-agent systems, and RAG architectures.
- Drive AI platform strategy leveraging Google Cloud (Vertex AI, GKE, Cloud Run).
- Lead architecture reviews, technical governance, and design standards.
2. LLM / SLM & Generative AI Solutions
- Architect solutions using commercial LLMs such as Gemini, GPT, and Claude.
- Design scalable systems using open-source models (Mixtral, Mistral, Gemma, Phi-3).
- Define strategies for fine-tuning (LoRA, QLoRA, PEFT) and model optimization.
- Oversee model evaluation frameworks and benchmarking (HELM, lm-eval, RAGAS).
3. Google AI Ecosystem Leadership
- Lead adoption of:
- Vertex AI for model lifecycle management
- Google Agent Development Kit (ADK) for intelligent agents
- Google Workspace integrations (Docs, Sheets, Gmail, Drive, Meet)
- Architect solutions using BigQuery, Lakehouse, and Vector Databases.
4. AI Platform & MLOps Architecture
- Design scalable MLOps pipelines for training, deployment, and monitoring.
- Define CI/CD strategies for AI systems using GitHub Actions / GitLab CI.
- Establish observability frameworks using LangSmith, MLflow, Weights & Biases.
- Optimize infrastructure cost and performance across cloud and hybrid environments.
5. Multi-Agent Systems & AI Frameworks
- Architect complex workflows using:
- LangChain, LlamaIndex, LangGraph
- Semantic Kernel for multi-agent orchestration
- Design intelligent automation pipelines and agent collaboration patterns.
6. Data & RAG Architecture
- Design enterprise RAG pipelines using Vertex AI Vector DB, ChromaDB.
- Define data ingestion, transformation, and governance strategies.
- Architect semantic search and knowledge retrieval systems.
7. Application & Integration Architecture
- Define backend architecture using FastAPI / Node.js APIs.
- Architect API management and security using Apigee / MuleSoft.
- Guide frontend architecture using React / Angular for AI-driven applications.
8. Engineering Leadership
- Provide technical leadership and mentorship to AI/ML engineers.
- Collaborate with product, data, and engineering teams for solution delivery.
- Lead design documentation, architecture diagrams, and technical roadmaps.
- Ensure adherence to coding standards, testing, and quality frameworks.
9. Deployment & Infrastructure
- Architect deployments across:
- GCP (Vertex AI, GKE, Cloud Run)
- Hybrid and on-prem environments
- Edge AI use cases
- Ensure scalability, reliability, and security of AI systems.
10. AI Governance & Responsible AI
- Define frameworks for AI ethics, bias mitigation, and explainability.
- Establish governance for model lifecycle, monitoring, and compliance.
- Implement safeguards for hallucination detection and output validation.
Required Qualifications
- 12–18 years of software engineering experience.
- 7 years in AI/ML with strong focus on Generative AI and LLMs.
- Deep expertise in Google AI ecosystem (Vertex AI, Gemini, ADK, AI Studio).
- Strong experience in LLMs, SLMs, RAG, and multi-agent architectures.
- Proficiency in Python and familiarity with Node.js.
- Hands-on experience with MLOps, CI/CD, and cloud-native architecture (GCP).
- Proven experience designing scalable, production-grade AI systems.
Preferred Qualifications
- Google Cloud Certifications (Professional ML Engineer / Cloud Architect).
- Experience contributing to open-source AI/ML projects.
- Expertise in edge AI and hybrid cloud deployments.
- Experience building enterprise AI platforms or COEs.
- Strong leadership experience mentoring and scaling AI teams.
Key Skills Summary
- Generative AI (LLMs, SLMs, RAG, Agents)
- Google Cloud AI Stack (Vertex AI, Gemini, ADK)
- AI Frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel)
- MLOps & Observability (MLflow, W&B, LangSmith)
- Cloud & Infrastructure (GCP, Kubernetes, Serverless)
- Backend & APIs (FastAPI, Node.js, Apigee)
- Data & Vector DBs (BigQuery, ChromaDB, Vector Search)