AI/ML Engineer

Think Consulting; Atlanta (GA); On-site; USD 120,000 - 180,000; Full time

Job summary

Think Consulting is seeking an AI/ML Engineer to build and support production-ready AI, ML, and intelligent automation solutions. The role spans AI engineering, data, application development, and automation to create scalable systems turning complex data into real-time insights and actions.

The ideal candidate combines strong Python and machine learning experience with hands-on knowledge of cloud environments, APIs, data pipelines, and modern generative AI technologies.

Qualifications

• 3+ years of professional experience in AI/ML engineering, data science, machine learning, or intelligent automation.
• 3+ years of hands-on experience with cloud-based compute, data, or AI technologies.
• Strong Python development skills.
• Hands-on experience with ML frameworks such as TensorFlow, PyTorch, and/or scikit-learn.
• Experience developing, deploying, and supporting machine learning solutions in production.
• Experience designing real-time, streaming, or event-driven data architectures.
• Working knowledge of REST APIs, microservices, and modern system integration patterns.
• Ability to work across technical disciplines to move AI solutions from concept through production.

Responsibilities

• Develop and deploy machine learning and AI solutions for production environments.
• Create intelligent applications and workflows that use AI to automate decisions and improve business processes.
• Design conversational AI and generative AI capabilities using large language models.
• Build and optimize LLM-based solutions with an emphasis on response quality, reliability, performance, and scalability.
• Develop predictive models using structured and unstructured datasets to identify patterns, trends, anomalies, and potential issues.
• Engineer secure integrations between AI applications, enterprise systems, data sources, and internal platforms.
• Build real-time, streaming, and event-driven data pipelines supporting AI and ML applications.
• Integrate AI-generated insights and recommendations into tools used by internal teams.
• Develop solutions that provide contextual recommendations, intelligent assistance, and dynamic knowledge retrieval.
• Create automated diagnostic and remediation workflows with appropriate security, governance, and audit controls.
• Implement monitoring capabilities for model performance, system health, automated workflows, and operational metrics.
• Partner with engineering, product, operations, and other technical stakeholders throughout solution design and implementation.
• Ensure AI and automation solutions follow established security, access-control, compliance, and authorization requirements.

Skills

Python development

TensorFlow

PyTorch

scikit-learn

Cloud computing

Real-time data pipelines

Data integration

Cross-functional collaboration

Tools

REST APIs

Microservices

Job description

Position Summary

We are seeking an AI/ML Engineer to build and support production-ready artificial intelligence, machine learning, and intelligent automation solutions. This individual will work across AI engineering, data, application development, and automation to create scalable systems that turn complex data into real-time insights and actions.

The ideal candidate combines strong Python and machine learning experience with hands‑on knowledge of cloud environments, APIs, data pipelines, and modern generative AI technologies. Experience applying AI within operational, customer‑facing, or workflow‑driven environments is especially valuable.

Key Responsibilities

• Develop and deploy machine learning and AI solutions for production environments.
• Create intelligent applications and workflows that use AI to automate decisions and improve business processes.
• Design conversational AI and generative AI capabilities using large language models.
• Build and optimize LLM‑based solutions with an emphasis on response quality, reliability, performance, and scalability.
• Develop predictive models using structured and unstructured datasets to identify patterns, trends, anomalies, and potential issues.
• Engineer secure integrations between AI applications, enterprise systems, data sources, and internal platforms.
• Build real‑time, streaming, and event‑driven data pipelines supporting AI and ML applications.
• Integrate AI‑generated insights and recommendations into tools used by internal teams.
• Develop solutions that provide contextual recommendations, intelligent assistance, and dynamic knowledge retrieval.
• Create automated diagnostic and remediation workflows with appropriate security, governance, and audit controls.
• Implement monitoring capabilities for model performance, system health, automated workflows, and operational metrics.
• Partner with engineering, product, operations, and other technical stakeholders throughout solution design and implementation.
• Ensure AI and automation solutions follow established security, access‑control, compliance, and authorization requirements.

Required Qualifications

• 3+ years of professional experience in AI/ML engineering, data science, machine learning, or intelligent automation.
• 3+ years of hands‑on experience working with cloud‑based compute, data, or AI technologies.
• Strong Python development skills.
• Hands‑on experience with ML frameworks such as TensorFlow, PyTorch, and/or scikit‑learn.
• Experience developing, deploying, and supporting machine learning solutions in production.
• Experience designing real‑time, streaming, or event‑driven data architectures.
• Working knowledge of REST APIs, microservices, and modern system integration patterns.
• Understanding of secure application and data‑integration practices.
• Ability to work across technical disciplines to move AI solutions from concept through production.

Preferred Experience

• Generative AI and large language model development.
• Retrieval‑Augmented Generation (RAG) architectures and knowledge retrieval solutions.
• LLM optimization, tuning, evaluation, or prompt/workflow development.
• Conversational AI or AI‑enabled user experiences.
• IoT, telemetry, connected‑device, or distributed sensor datasets.
• Contact center technology, customer interaction platforms, or enterprise workflow automation.
• Authentication, authorization, identity, and access‑control concepts.
• AI/ML monitoring, observability, governance, and responsible deployment practices.

Equal Opportunity Employer, including disability and protected veteran status