AI / ML Engineer – OrcaWorks AI

Qualifications

• Master’s degree (or Bachelor’s with equivalent experience) in AI, ML, or Data Science
• Proficiency in Python and libraries such as Scikit-learn, TensorFlow, PyTorch
• Familiarity with MLflow, Docker, Kubeflow, and Vespa/ElasticSearch for search integration
• Understanding of applied AI architecture and hybrid workflows involving structured and unstructured data
• Analytical mindset and eagerness to solve real-world AI challenges

Responsibilities

• Build, train, and deploy machine learning models for predictive and generative AI applications
• Collaborate with cross-functional teams to design applied AI solutions aligned with business use cases
• Implement model monitoring, retraining, and optimization pipelines using Prefect, Airbyte, and MLflow
• Integrate LLMs and embeddings into production systems via RAG pipelines and agent frameworks
• Document findings, model versions, and evaluation results

Full Description

Location: Hybrid / Atlanta, GA

Experience Level: Entry-level (Master’s preferred)

About OrcaWorks AI

OrcaWorks AI is pioneering applied AI systems designed for real-world business intelligence and automation.

We’re hiring AI/ML Engineers eager to innovate and shape the next generation of machine learning and LLM-integrated applications.

Key Responsibilities

• Build, train, and deploy machine learning models for predictive and generative AI applications.

• Collaborate with cross-functional teams to design applied AI solutions aligned with business use cases.

• Implement model monitoring, retraining, and optimization pipelines using Prefect, Airbyte, and MLflow.

• Integrate LLMs and embeddings into production systems via RAG pipelines and agent frameworks.

• Document findings, model versions, and evaluation results.

Required Skills & Qualifications

• Master’s degree (or Bachelor’s with equivalent experience) in AI, ML, or Data Science.

• Proficiency in Python and libraries such as Scikit-learn, TensorFlow, PyTorch.

• Familiarity with MLflow, Docker, Kubeflow, and Vespa/ElasticSearch for search integration.

• Understanding of applied AI architecture and hybrid workflows involving structured and unstructured data.

• Analytical mindset and eagerness to solve real-world AI challenges.