AI Engineer / Application Analyst

Southern CompanyBirmingham, AL
Hybrid

About The Position

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.

Requirements

  • 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
  • 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.
  • Proficiency in Python and AI frameworks for LLMs and agents.
  • 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.

Nice To Haves

  • Proficiency with Databricks preferred.

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

Benefits

  • competitive base salary
  • annual incentive awards for eligible employees
  • health, welfare and retirement benefits designed to support physical, financial, and emotional/social well-being
  • relocation assistance
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