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oracle

AI Engineer

Southern Company Services
Birmingham, USA
Full-time
Mid
Onsite
Discovered Today
databrickspython

Full Job Posting

Qualifications

  • 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
  • This position falls under the company’s Insider Threat Program and will have access to, and control over sensitive data, systems or assets. Enhanced personnel screening, which includes a background review, drug screen and psychological assessment, will be required if you are selected for this position
  • (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
  • 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.

Responsibilities

  • 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.

Preferred Qualifications

  • Proficiency with Databricks preferred.

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