Machine Learning Engineer III 4P/791

Job summary

Position: Machine Learning Engineer III – AI/ML Product Engineering Location: Atlanta, GA Duration: 5 Months Client: Southern Company Services Southern Company Services is seeking an experienced Machine Learning Engineer III to develop scalable, reusable, and production-grade AI products for deployment across multiple operating companies.

Qualifications

• Southern Company Services is seeking an experienced Machine Learning Engineer III to develop scalable, reusable, and production-grade AI products for deployment across multiple operating companies.
• Provide technical guidance to teams adopting shared AI products and components.
• Required
• Strong experience developing and deploying production-grade AI and machine learning solutions.
• Hands-on experience with RAG architectures, LLM applications, multi-agent systems, and NLP.
• Experience with Azure AI services, Google Cloud Platform AI services, or Azure Machine Learning.
• Proficiency with Python and frameworks such as PyTorch, Transformers, or LangChain.
• Experience deploying scalable models and AI services in cloud environments.
• Knowledge of APIs, software engineering practices, model monitoring, and MLOps.
• Experience working with structured and unstructured datasets.
• Strong communication, collaboration, analytical, and problem-solving
• Experience with Databricks, vector databases, embeddings, and semantic search.

Responsibilities

• Design and build modular, reusable AI components and services.
• Develop scalable RAG solutions using structured and unstructured data.
• Engineer multi-agent systems for task coordination, workflow automation, and decision support.
• Build transcription and NLP pipelines for customer-interaction analysis.
• Develop and fine-tune models using PyTorch, Hugging Face Transformers, LangChain, or similar frameworks.
• Package and deploy models using Azure Machine Learning, Google Cloud Platform, or Databricks.
• Integrate Databricks for data ingestion, feature engineering, experimentation, and model development.
• Develop reusable libraries, APIs, templates, and engineering patterns.
• Partner with MLOps, DevOps, data engineering, architecture, and product teams.
• Implement monitoring for model performance, data drift, system usage, and operational reliability.
• Ensure AI solutions meet enterprise security, privacy, compliance, scalability, and observability

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