Staff IT AI Implementation Engineer

Spherion Staffing & Recruiting, Milpitas CA; San Jose (CA); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Spherion Staffing & Recruiting, Milpitas CA, is seeking a Staff IT AI Implementation Engineer to define and own the company’s AI services architecture. You will manage enterprise AI platforms, build production-grade AI agents, and lead governance with IT leadership to ensure compliant, scalable AI solutions.

You’ll work at the intersection of hands-on delivery and strategic policy, partnering with IT Security to implement responsible AI, guardrails, and cross-functional enablement across

Qualifications

• 3+ years in software engineering, data science, ML engineering, or applied AI; or 2+ years building production LLM apps.
• 1-3 years with AI models, LLMs, or AI agent frameworks.
• Proven experience building AI agents with tool use, API integrations, and MCP.
• Advanced, production-level prompt engineering and context management.
• Experience evaluating LLM performance and regression testing.
• Knowledge of AI security, data privacy, and OWASP Top 10 for LLMs.
• Containerizing AI apps and deploying self-hosted inference.
• Hands-on with managed cloud AI services (AWS/Azure/Google).
• Familiarity with AI governance frameworks (NIST RMF, ISO 42001).
• Ability to translate complex concepts for non-technical stakeholders.

Responsibilities

• Enterprise Platform Management: administer AI platforms and control access, security, and cost.
• Agent & Workflow Automation: design, build, deploy AI agents and MCP integrations.
• RAG & Systems Development: architect and maintain vector DBs, embeddings, retrieval quality.
• Model Serving & Containerization: deploy and containerize models in cloud or local env.

Skills

Production LLM apps

AI agents

Model Context Protocol

Prompt engineering

LLM evaluation

AI security

Cloud AI services

Tools

LangChain

LangGraph

AutoGen

Ollama

Docker

Kubernetes

AWS Bedrock

Azure OpenAI

Vertex AI

Job description

Location: On-site / Hybrid (must be local)

Join an innovative, rapidly growing medical technology and surgical robotics pioneer dedicated to transforming patient outcomes through cutting-edge technology. Our culture is fueled by curiosity, conviction, and technical excellence. We offer an environment where every employee directly impacts our mission - fostering continuous professional growth, collaborative management, and high-impact work in a supportive, mission-driven environment.

The Opportunity

This is a unique opportunity to define and build the enterprise AI ecosystem from the ground up. As the Staff IT AI Implementation Engineer, you will own the company’s AI services architecture - ranging from enterprise LLM platforms (e.g., Claude Enterprise, ChatGPT Enterprise) to the autonomous agents and automated workflows that make AI actionable across all business units.

This role sits squarely at the intersection of hands-on technical execution and strategic influence. You will build and deploy production-grade AI applications while collaborating closely with IT Leadership and IT Security to shape responsible AI governance policies. If you are energized both by building production-ready AI agents and by guiding business units on compliant AI usage, this role is built for you.

Key Responsibilities

• Enterprise Platform Management: Administer and manage enterprise AI platforms (e.g., Claude Enterprise, ChatGPT Enterprise), overseeing access controls, security settings, usage tracking, and cost allocation in close coordination with IT.
• Agent & Workflow Automation: Design, build, deploy, and maintain custom AI agents, Model Context Protocol (MCP) integrations, API connections, and automated workflows to solve cross-departmental business challenges.
• RAG & Systems Development: Architect and maintain Retrieval-Augmented Generation (RAG) pipelines, including vector databases, embedding strategies, chunking methodologies, and retrieval quality evaluation.
• Model Serving & Containerization: Deploy, serve, and containerize local or cloud-hosted models (e.g., Docker, Ollama, self-hosted inference).

Governance, Security & Quality Assurance

• AI Security & Guardrails: Enforce least-privilege access and mitigate AI-specific security risks (prompt injection, data leakage, PII handling) adhering to OWASP Top 10 for LLM Applications and internal IT Security standards.
• Testing & Evaluation: Implement LLM evaluation systems, including test set creation, LLM-as-judge frameworks, and regression testing for prompts and agents.
• Governance Policy Development: Partner with IT Leadership and Security to develop, maintain, and continuously refine enterprise AI acceptable-use policies and governance frameworks (e.g., NIST AI RMF, ISO/IEC 42001).
• Vendor & Security Reviews: Evaluate emerging AI tools and platforms with IT Security input; submit all agents, integrations, MCP servers, and data connectors for security review prior to production release.

Stakeholder Engagement & Enablement

• Cross-Functional Partner: Serve as the primary IT point of contact for AI requests, scoping use cases, evaluating technical feasibility, and delivering compliant solutions for non-technical stakeholders.
• Enablement & Training: Provide organizational training, guidelines, and documentation on effective, secure, and compliant AI tool usage.

Key Qualifications

Required Qualifications

• Experience:
• 3+ years in software engineering, data science, ML engineering, or applied AI; OR 2+ years specifically building production LLM applications.
• 1-3 years of hands-on experience developing with AI models, large language models (LLMs), or AI agent frameworks.
• Technical Execution:
• Proven experience building AI agents with tool use / function calling, API integrations, and Model Context Protocol (MCP).
• Advanced, production-level prompt engineering and context management skills.
• Experience evaluating LLM performance (LLM-as-judge, prompt/agent regression testing, unit testing).
• Security & Infrastructure:
• Working knowledge of LLM application security (prompt injection defense, guardrails, PII handling, OWASP Top 10 for LLMs).
• Experience containerizing AI applications and deploying/serving models (including self-hosted inference).
• Hands-on experience with managed cloud AI services (AWS Bedrock, Azure OpenAI / AI Foundry, or Google Vertex AI).
• Governance & Cross-Functional Skills:
• Familiarity with AI governance frameworks (e.g., NIST AI RMF, ISO/IEC 42001) and responsible-AI practices.
• Demonstrated track record of deploying enterprise AI solutions and communicating complex technical concepts to non-technical teams.

Preferred / Nice-to-Have Qualifications

• Certifications:
• AWS Certified AI Practitioner (AIF-C01) or AWS Certified Generative AI Developer - Professional
• Microsoft Azure AI Fundamentals (AI-901) or CompTIA AI+
• Frameworks & Tools:
• Familiarity with agent orchestration frameworks (LangChain, LangGraph, AutoGen).
• Observability and cost-tracking experience (LangSmith, token tracking, latency monitoring).
• Open-source local model tools (Ollama, OpenWebUI) and model fine-tuning techniques.
• Personal Projects: Demonstrated personal AI builds, home labs, or personal open-source projects outside of work.
• Adherence to relevant Quality Management System (QMS) requirements, environmental health and safety (EHS) policies, and regulatory compliance standards applicable to high-tech, regulated medical environments.