AI Engineer / Application Analyst

AI Engineer, or Application Analyst

Location

The Energy Center, Birmingham, AL

4 days in-office / 1 day remote

Designs, builds, and evolves AI solutions that deliver measurable business and operational value across Southern Power. The AI Engineer translates use-case intent into production-ready agents, models, and workflows, working within the enterprise architectural, governance, and operational standards to ensure solutions are scalable, safe, and fit for a critical-infrastructure environment.

JOB REQUIREMENTS

(Experience, Knowledge, Skills)

Domain Expertise

• Experience building AI and machine learning applications in enterprise settings.
• Practical understanding of agentic AI concepts, including planners, tool-calling, memory, and multi-agent collaboration.
• Familiarity with enterprise data platforms and APIs, including structured, unstructured, and document-based data.
• Awareness of AI governance, risk, and compliance considerations in regulated environments

Individual Skills

• Strong problem-solving skills with the ability to decompose complex use cases into modular agent

behaviors.

• Product-oriented mindset, focused on delivering tangible outcomes rather than experimentation alone.
• Ability to work effectively within defined architectural and governance constraints.
• Clear communicator who can explain AI behavior and limitations to non-technical stakeholders.

Technical Skills

• Proficiency in Python and AI frameworks for LLMs and agents.
• Proficiency with Databricks preferred.
• Experience with prompt engineering, evaluation techniques, and structured outputs.
• Familiarity with vector databases, embeddings, and retrieval-augmented generation (RAG) patterns.
• Working knowledge of API integration and tool execution within agent workflows.
• Exposure to CI/CD, model deployment, and observability concepts sufficient to operate within pipelines.

MAJOR JOB RESPONSIBILITIES

• Develop AI solutions. Build and configure agents that reason, plan, and act using models, tools, and enterprise data, aligned to approved patterns.
• Implement use-case workflows end-to-end. Translate business and operational requirements into AI workflows spanning data ingestion, inference, tool execution, and human-in-the-loop review.
• Apply responsible AI and guardrails. Embed safety controls, validation steps, and explainability mechanisms into agent logic and prompts by design.
• Collaborate across the AI delivery lifecycle. Partner with architects on design decisions and with ML Ops to ensure smooth deployment, monitoring, and iteration.
• Optimize performance and cost. Tune prompts, models, and agent interactions to improve accuracy, latency, and cost efficiency.
• Support experimentation to production. Rapidly prototype new AI capabilities while adhering to standards that enable promotion to enterprise-grade deployments.
• Continuously improve agent behavior. Use feedback, evaluations, and operational signals to refine agent performance and reliability over time

ADDITIONAL DETAILS

• Some relocation assistance is available if you do not already live within a reasonable commute distance.