AI Engineer (Enterprise AI Enablement & Engineering)

We are seeking an AI Engineer to design, build, and scale AI-powered solutions across the enterprise. This role sits within the AI Engineering function and is responsible for translating business needs into production-grade AI systems, with a primary focus on LLM-based solutions (e.g., ChatGPT/OpenAI) while maintaining flexibility to integrate emerging AI platforms.

You will work closely with AI Enablement, IT, and business stakeholders to deliver secure, scalable, and high-impact AI capabilities that improve productivity and drive measurable business outcomes.

Key Responsibilities

AI Solution Development & Engineering

- Design and implement AI-powered workflows and applications using LLMs (ChatGPT/OpenAI, and future platforms)
- Build and maintain agent-based systems, orchestration layers, and prompt frameworks
- Develop APIs and services to integrate AI into enterprise systems (e.g., ServiceNow, internal tools, data platforms)
- Implement RAG (Retrieval-Augmented Generation) architectures using enterprise data sources
- Ensure solutions are modular, reusable, and scalable

AI Architecture & Integration

- Define and implement AI system architecture , including model selection, routing, and orchestration
- Integrate AI capabilities into existing enterprise ecosystems
- Partner with security and data teams to enforce:
- Data boundaries
- Access controls
- Compliance requirements

Critical Principle: AI architecture, data boundaries, and model control remain internal and are never outsourced. Prompt Engineering & Optimization

- Develop and maintain enterprise prompt libraries and reusable frameworks
- Optimize prompts and agent flows for:




- Accuracy
- Consistency
- Cost efficiency
- Collaborate with AI Enablement to standardize prompt patterns across teams

Evaluation & Performance Improvement

- Design and execute evaluation frameworks (e.g., OpenAI Evals, DeepEval)
- Build automated pipelines to test:
- Accuracy
- Reliability
- Edge-case handling
- Continuously monitor and improve model performance in production

AI Use Case Enablement

- Partner with business stakeholders to:
- Identify high-value AI use cases
- Translate requirements into technical solutions
- Rapidly prototype and iterate on AI solutions

Required Qualifications Education & Experience

- Bachelor’s degree in Computer Science, Computer Engineering, or related field
- 1–4+ years of experience in software engineering or AI/ML engineering (adaptable based on capability)
- Hands-on experience building AI/LLM-based applications

Technical Skills

- Programming: Python (required), JavaScript/TypeScript (preferred)
- Frameworks & Tools:
- LLM tooling: OpenAI APIs, LangChain/LangGraph, Flowise (or similar)
- Backend: FastAPI, Node.js
- Frontend (nice to have): React
- Data & AI:
- Pandas, NumPy, basic ML concepts
- Experience with RAG pipelines and vector databases
- DevOps & Engineering:
- Git/GitHub
- API design and integration




- Containerization (Docker preferred)

AI-Specific Experience

- Experience building AI agents, chatflows, or automation workflows
- Familiarity with evaluation frameworks and testing methodologies
- Understanding of prompt engineering and LLM behavior

Preferred Qualifications

- Experience integrating AI into enterprise environments
- Exposure to ServiceNow, ITSM workflows, or enterprise support systems
- Knowledge of data pipelines and knowledge management systems
- Familiarity with AI governance, security, and compliance considerations
- Experience working in Agile/Scrum environments

Soft Skills

- Strong problem-solving and systems thinking
- Ability to translate ambiguous business problems into technical solutions
- Effective communication with both technical and non-technical stakeholders
- Proactive, adaptable, and able to operate in a fast-evolving AI landscape

Success Metrics

- Adoption and usage of AI solutions across the business
- Measurable productivity gains and efficiency improvements
- Reliability and performance of deployed AI systems
- Reusability of frameworks and reduction in duplicate solutions

Team Context This role is part of the AI Engineering team , working alongside:

- AI Specialists (Enablement & Operations) – onboarding, support, and adoption
- AI Engineers (this role) – architecture, development, and integration

Strategic Direction

- Primary platform: ChatGPT/OpenAI
- Architecture designed for multi-model flexibility (future vendors/models)
- Strong emphasis on:
- Internal ownership of AI systems
- Secure enterprise integration
- Scalable, reusable frameworks

📌 Artificial Intelligence Engineer (Tijuana)
🏢 ITJ
📍 Tijuana