Machine Learning Engineer III 4P/791

4P Consulting•Atlanta, GA

7d•Onsite

About The Position

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. This role will focus on Retrieval-Augmented Generation, multi-agent systems, natural language processing, model deployment, and cloud-based AI solutions. The ideal candidate will have strong software engineering skills, hands-on AI/ML experience, and expertise with Azure or Google Cloud Platform.

Requirements

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 skills.

Nice To Haves

Experience with Databricks, vector databases, embeddings, and semantic search.
Experience building reusable enterprise AI platforms or shared AI services.
Knowledge of model evaluation, data drift, observability, and responsible AI.
Familiarity with CI/CD, containers, Kubernetes, and cloud-native deployment.
Utility, energy, or regulated-industry experience is preferred.

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 requirements.
Provide technical guidance to teams adopting shared AI products and components.