AI Engineer

Brooksource Fortune 500 Energy Client Houston, TX

Overview

We are hiring multiple AI Engineers to help build and scale enterprise AI solutions across functions including Supply Chain, Finance, HR, and other business operations. These teams are developing agentic AI workflows that automate complex business processes, improve decision-making, and drive operational efficiency.

This role is ideal for engineers who combine software engineering and data science expertise, have hands-on experience building GenAI applications, and can demonstrate how they've successfully moved AI solutions from proof-of-concept into production.

Engineers will work within small, agile AI pods consisting of a Lead AI Engineer and two AI Engineers focused on delivering business-critical AI capabilities.

Key Responsibilities

• Design, develop, and deploy AI agents and agentic workflows to solve real business challenges
• Build Retrieval-Augmented Generation (RAG) systems, including data ingestion, retrieval, orchestration, and response generation
• Develop multi-agent and workflow orchestration solutions that leverage:
  • Tool calling
  • Memory management
  • Multi-step reasoning
  • Decision-making logic
• Partner with business stakeholders to understand workflows and identify automation opportunities
• Implement evaluation frameworks to monitor:
  • Accuracy
  • Reliability
  • Hallucination rates
  • User adoption and business impact
• Develop production-ready AI applications, APIs, and integrations
• Build monitoring, testing, and observability capabilities for AI systems
• Collaborate with AI Leads and cross-functional teams on architecture, best practices, and platform standardization
• Continuously leverage emerging AI capabilities and tools to improve development efficiency and accelerate solution delivery

Required Qualifications

• 3-6 years of experience in Software Engineering, Data Science, Machine Learning, or a related field
• Hands-on experience designing and building AI agents, copilots, or agentic workflows
• Demonstrated success deploying GenAI solutions into production environments
• Strong software engineering fundamentals with expertise in Python
• Experience building applications powered by Large Language Models (LLMs)
• Experience with workflow orchestration frameworks such as LangGraph, CrewAI, Semantic Kernel, AutoGen, or similar technologies
• Ability to clearly explain:
  • What was built
  • Why specific architectural decisions were made
  • How trust, accuracy, and reliability were established
• Strong problem-solving skills and a mindset of ownership and accountability
• Ability to work independently in a fast-paced, highly collaborative environment

Preferred Qualifications

• Experience with Microsoft AI technologies such as Azure OpenAI, Azure AI Foundry, Microsoft Copilot Studio, or Semantic Kernel
• Experience with vector databases and RAG architectures
• Experience working with enterprise data platforms such as Databricks, Snowflake, or Microsoft Fabric
• Familiarity with AI evaluation, experimentation, and observability tools such as LangSmith, MLflow, Arize, Weights & Biases, or similar platforms
• Understanding of machine learning, model performance evaluation, and MLOps concepts
• Experience integrating AI solutions into enterprise applications and business processes

What Makes Someone Successful

The ideal candidate demonstrates:

• Extreme ownership and accountability
• A builder mentality with a bias toward action
• Strong communication skills with both technical and business stakeholders
• Curiosity and a passion for applying AI to solve real-world business problems
• The ability to quickly learn new tools and technologies rather than relying on a specific platform or framework
• A track record of taking AI initiatives from idea to measurable business impact