AI Engineer/Forward Engineer

Job Title: AI Engineer/Forward Engineer

Location: Phoenix, AZ

Employment Type: Full-time

Job Description:

AI Engineer/Forward Engineer

Must Have Technical/Functional Skills

Preferred Qualifications

Experience building enterprise AI applications using OpenAI, Anthropic, Gemini, or similar LLMs.

Hands-on experience with LangGraph, LangChain, LlamaIndex, Semantic Kernel, or comparable frameworks.

Experience deploying production-grade AI agents and RAG solutions.

Knowledge of AI observability, evaluation, and monitoring tools.

Experience implementing secure, governed, and scalable AI solutions.

Desired Competencies

Strong problem-solving and system design skills.

Ability to translate business requirements into AI-powered solutions.

Excellent communication and stakeholder management skills.

Collaborative mindset with experience working in cross-functional teams.

Passion for emerging AI technologies and continuous learning.

Roles & Responsibilities

We are seeking an innovative Agentic AI Engineer / Forward Deployed AI Engineer to design, develop, and deploy AI-powered applications that leverage Large Language Models (LLMs) to solve real-world business challenges. This role focuses on building intelligent agentic systems and production-grade AI solutions rather than training foundation models.

The ideal candidate will have expertise in AI application development, prompt engineering, retrieval-augmented generation (RAG), orchestration frameworks, and scalable AI services. You will work closely with business stakeholders and engineering teams to deliver reusable AI capabilities that automate repetitive tasks and enhance operational efficiency.

Key Responsibilities

Agentic AI Engineering

Design and develop autonomous AI agent workflows capable of executing multi-step business processes.

Build AI systems around foundation models without requiring model training or pre-training.

Develop advanced prompt engineering and context engineering strategies to improve AI performance and reliability.

Implement tool use, function calling, and API integrations for AI agents.

Build agent orchestration using frameworks such as LangGraph, agent loops, planners, and multi-agent architectures.

Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases.

Fward Deployed AI Engineering

Partner with business stakeholders to identify AI use cases and rapidly develop production-ready AI applications.

Design, develop, and deploy AI-powered applications using modern LLM frameworks.

Build scalable RAG pipelines for enterprise knowledge retrieval.

Optimize prompts, workflows, and evaluation frameworks to improve AI solution quality.

Develop reusable AI services, APIs, and components that can be leveraged across multiple teams.

Integrate AI solutions with enterprise systems, data sources, and business applications.

Support deployment, monitoring, and continuous improvement of AI applications in production.