Artificial Intelligence Engineer

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 (flexible 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