Machine Learning Engineer III

 Machine Learning Engineer III – AI/ML Engineer

Location: Atlanta, GA

Client- Southern Company services

Job Summary

We are seeking an experienced Machine Learning Engineer III / AI-ML Engineer to support the development of reusable, scalable AI products that can be deployed across multiple operating companies and business units.

This role will focus on building production-grade AI solutions, including Retrieval-Augmented Generation (RAG), multi-agent orchestration, NLP pipelines, transcription solutions, model deployment, and reusable AI components for internal operational workflows.

The ideal candidate will have strong hands-on experience with GCP or Azure AI services, modern ML frameworks, strong software engineering skills, and a product-focused mindset.

Key Responsibilities

• Design and build modular, reusable AI components that can scale across business units.
• Lead development of scalable RAG-based solutions for document comparison and analysis.
• Work with structured and unstructured data to support AI-driven business solutions.
• Engineer multi-agent systems for intelligent task coordination and decision support.
• Develop transcription and NLP pipelines for customer interaction analysis.
• Build, fine-tune, and deploy models using tools and frameworks such as PyTorch, Transformers, and LangChain.
• Package models for deployment in GCP, Azure ML, and/or Databricks.
• Integrate with Databricks for data ingestion, feature engineering, experimentation, and model development.
• Work closely with MLOps, DevOps, and Data Engineering teams to align infrastructure and deployment patterns.
• Contribute to shared libraries, APIs, templates, and reusable frameworks that accelerate AI product delivery.
• Provide technical guidance to teams adopting reusable AI components.
• Ensure AI products meet enterprise-grade security, compliance, scalability, and maintainability standards.
• Implement monitoring for model performance, data drift, usage metrics, and production reliability.

Required Qualifications

• Experience as a Machine Learning Engineer, AI Engineer, Data Scientist, or similar technical role.
• Strong experience building production-grade AI/ML solutions.
• Hands-on experience with cloud-based AI services, preferably GCP or Azure.
• Experience developing RAG-based applications using structured and unstructured data.
• Strong knowledge of machine learning, NLP, LLMs, and modern AI application patterns.
• Experience with frameworks and tools such as:
  • PyTorch
  • Transformers
  • LangChain
• Experience deploying models in cloud or enterprise environments.
• Strong programming and software engineering skills.
• Ability to work with APIs, reusable components, and scalable architectures.
• Experience collaborating with MLOps, DevOps, and data engineering teams.
• Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

• Experience with Azure ML, GCP Vertex AI, Databricks, or similar platforms.
• Experience designing multi-agent systems or AI orchestration workflows.
• Experience developing transcription, NLP, or customer interaction analytics pipelines.
• Experience with model monitoring, data drift detection, observability, and usage metrics.
• Experience building shared AI libraries, reusable templates, or internal AI platforms.
• Understanding of enterprise security, compliance, and governance requirements for AI products.
• Product mindset with the ability to design AI solutions that are reusable, scalable, and business-focused.

Key Skills

Machine Learning, AI Engineering, RAG, LLMs, NLP, Multi-Agent Systems, PyTorch, Transformers, LangChain, Azure ML, GCP, Databricks, MLOps, DevOps, Model Deployment, Feature Engineering, Data Ingestion, APIs, Model Monitoring, Data Drift, AI Product Development, Security and Compliance.

Ideal Candidate

The ideal candidate is an experienced AI/ML engineer with strong engineering skills and hands-on experience building scalable AI solutions. This person should be comfortable working with cloud AI platforms, RAG applications, multi-agent workflows, Databricks, NLP pipelines, and production deployment practices.