{"id":3282189,"url":"https://alion.io/job/southern-ai-engineer","title":"AI Engineer","company":{"id":2607898,"name":"Southern","domain":"southern.com","url":"https://alion.io/company/southern-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Atlanta, United States","Birmingham, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":123000,"max_usd":273000,"period":"year","method":"role_country_seniority_unknown","sample_n":4425},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Embeddings","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Structured Outputs","optional":false}],"status":"live","first_seen_at":"2026-10-26T14:23:37Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-10-29T01:52:28Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"AI Engineer\nLevels 5-7\nLocation: The Energy Center, Birmingham, AL or Ivan Allen, Atlanta, GA\n4 days in-office / 1 day remote\nDesigns, 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.\nJOB REQUIREMENTS: (Experience, Knowledge, Skills)\nDomain Expertise\nExperience building AI and machine learning applications in enterprise settings.\n\nPractical understanding of agentic AI concepts, including planners, tool-calling, memory, and multi-agent collaboration.\n\nFamiliarity with enterprise data platforms and APIs, including structured, unstructured, and document-based data.\n\nAwareness of AI governance, risk, and compliance considerations in regulated environments\n\nIndividual Skills:\nStrong problem-solving skills with the ability to decompose complex use cases into modular agent behaviors.\n\nProduct-oriented mindset, focused on delivering tangible outcomes rather than experimentation alone.\n\nAbility to work effectively within defined architectural and governance constraints.\n\nClear communicator who can explain AI behavior and limitations to non-technical stakeholders.\n\nTechnical Skills\nProficiency in Python and AI frameworks for LLMs and agents.\n\nProficiency with Databricks preferred.\n\nExperience with prompt engineering, evaluation techniques, and structured outputs.\n\nFamiliarity with vector databases, embeddings, and retrieval-augmented generation (RAG) patterns.\n\nWorking knowledge of API integration and tool execution within agent workflows.\n\nExposure to CI/CD, model deployment, and observability concepts sufficient to operate within pipelines.\n\nMAJOR JOB RESPONSIBILITIES:\nDevelop AI solutions. Build and configure agents that reason, plan, and act using models, tools, and enterprise data, aligned to approved patterns.\nImplement 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.\nApply responsibleAI and guardrails. Embed safety controls, validation steps, and explainability mechanisms into agent logic and prompts by design.\nCollaborate across the AI delivery lifecycle. Partner with architects on design decisions and with ML Ops to ensure smooth deployment, monitoring, and iteration.\nOptimize performance and cost. Tune prompts, models, and agent interactions toimprove accuracy, latency, and cost efficiency.\nSupport experimentation to production. Rapidly prototype new AI capabilities while adhering to standards that enable promotion to enterprise-grade deployments.\nContinuously improve agent behavior. Use feedback, evaluations, and operational signals to refine agent performance and reliability over time\nThis position falls under the company’s Insider Threat Program and will have access to, and control over sensitive data, systems or assets. 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